Image evaluation apparatus and camera

ABSTRACT

To provide an image evaluation apparatus and camera which are capable of evaluating an image which is comprehensively good. An image evaluation apparatus comprising: a storage portion which stores a plurality of images when an imaging portion of a camera has continuously imaged in a predetermined time interval a plurality of frames of a subject in a photographing area of the camera, and an image evaluation portion which evaluates a plurality of the images stored in the storage portion, based on a characteristic amount showing a state in which the subject in the photographing area changing in chronological sequence, when the plurality of the images is imaged.

BACKGROUND OF THE INVENTION

This is a Division of application Ser. No. 12/588,398 filed Oct. 14,2009, which claims the priority to Japanese Patent Application Nos.2009-237106, filed Oct. 14, 2009, and 2008-267499, filed Oct. 16, 2008.The disclosure of the prior applications is hereby incorporated byreference herein in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image evaluation apparatus forevaluating an image photographed by a camera, and to a camera providedwith this image evaluation apparatus.

2. Description of the Related Art

In the prior art, an image evaluation apparatus which analyzes andautomatically evaluates a photographed image and automatically selects apreferable image is known (for example, refer to Japanese UnexaminedPatent Publication No. 2000-259833).

SUMMARY OF THE INVENTION

The automatic evaluation of the prior art does not carry out anevaluation of the overall image, such as selection of an imagerecognizing the characteristics of the face. Because of this, it doesnot necessarily select a comprehensively good image. In this situation,it is desirable to comprehensively evaluate good images.

The objective of the present invention is to provide an image evaluationapparatus and camera which are capable of evaluating an image which iscomprehensively good.

According to one aspect of the present invention, there is provided animage evaluation apparatus comprising: a storage portion which stores aplurality of images when an imaging portion of a camera has continuouslyimaged in a predetermined time interval a plurality of frames of asubject in a photographing area of the camera, and an image evaluationportion which evaluates a plurality of the images stored in the storageportion, based on a characteristic amount showing a state in which thesubject in the photographing area changing in chronological sequence,when the plurality of the images is imaged.

The image evaluation portion may calculate, from a plurality of theimages stored in the storage portion, a frequency distribution of motionvector arising between frames of each segmented region when each imageis segmented into a plurality of segmented regions, and evaluate thefrequency distribution of the motion vectors as the characteristicamount.

The storage portion may store a defocus amount of each image along withthe image, the defocus amount is the one obtained when a plurality ofthe images have been imaged, and the image evaluation portioncalculates, from a plurality of the images stored in the storageportion, a frequency distribution of a state of change in the defocusamount arising between frames of each segmented region when each imageis segmented into a plurality of segmented regions, and evaluates thefrequency distribution of the state of change of the defocus amount asthe characteristic amount.

The storage portion may store a defocus amount of each image along withthe image, the defocus amount is the one obtained when a plurality ofthe images have been imaged, and the image evaluation portion calculate,from a plurality of the images stored in the storage portion, a relativemotion state in the photographing area of the subject, based on changesof the defocus amount detected at a plurality of focus detectionpositions set in the photographing area, and evaluate the relativemotion state as the characteristic amount.

The image evaluation portion, from a plurality of the images stored inthe storage portion, may calculate a number of peak frequencies whichform a frequency distribution of motion vector arising between frames ofeach segmented region when each image is segmented into a plurality ofsegmented regions, and which have a frequency equal to or greater than athreshold frequency, and carry out the evaluation based on the number ofthe peak frequencies, and the image evaluation portion give a relativelyhigher evaluation with respect to other images, for an evaluation of animage imaged within a predetermined time from the change of the numberof the peak frequencies which form the frequency distribution of themotion vectors and which have a frequency equal to or greater than thethreshold frequency

The image evaluation portion may evaluate an image stored in the storageportion based on the characteristic amount, and the main subjectestimated from the image.

The image evaluation portion, from a plurality of the images stored inthe storage portion, may calculate as the characteristic amount afrequency distribution of motion vector arising between frames of eachsegmented region when each image is segmented into a plurality ofsegmented regions, and also carry out the evaluation based on similaritybetween the frequency distribution of the motion vectors and a modelfrequency distribution of motion vector set beforehand as an evaluationstandard of a preferable image.

The image evaluation portion may carry out the evaluation based onsimilarity between the frequency distribution of the motion vectors in aregion in a vicinity of the center of the image, and the model frequencydistribution of the motion vectors set beforehand as an evaluationstandard of a preferable image in the region in the vicinity of thecenter of the image.

The image evaluation portion may calculate as the characteristic amountthe frequency distribution of the motion vectors for a region in thevicinity of the center of the image, which has similarity of a color toa region and is not background, and which has continuity of the colorwith that region.

The image evaluation portion may delete motion components of aphotographing area included in the frequency distribution of the motionvectors.

The image evaluation portion may convert the frequency distribution ofthe motion vectors and the model frequency distribution of the motionvectors into respective two dimensional graphs, and also segmentsdeveloped regions of the two dimensional graphs into a plurality ofsegmented regions, calculate an absolute value of a frequency differencefor each corresponding segmented region for the frequency distributionof the motion vectors and the model frequency distribution of the motionvectors set beforehand as an evaluation standard of a preferable image,which have been converted into two dimensional graphs, and takes the sumof the absolute values as the similarity.

The image evaluation portion may take as the characteristic amount adispersion in a frequency distribution of motion vector arising betweenframes of each segmented region when the image is segmented into aplurality of segmented regions, and carry out evaluation based onsimilarity between the dispersion in the frequency distribution of themotion vectors and a dispersion in a model frequency distribution ofmotion vector set beforehand as an evaluation standard of a preferableimage.

The image evaluation portion, from among the frequency distributions ofthe motion vectors may calculate from a plurality of the images,extracts a frequency distribution of the motion vectors formed based ona peak frequency having a frequency amount equal or greater than athreshold frequency, and carry out evaluation based on similaritybetween the dispersion of the frequency distribution of the motionvectors and the dispersion in the model frequency distribution of themotion vectors set beforehand as an evaluation standard of a preferableimage.

The image evaluation portion may calculate, from a plurality of theimages stored in the storage portion, a frequency distribution of motionvector arising between frames of each segmented region when each imageis segmented into a plurality of segmented regions, and also extractsthe frequency distribution of the motion vectors formed by a peakfrequency having a frequency amount equal to or greater than a thresholdfrequency, and carry out evaluation based on a change amount of thefrequency amount of the peak frequency included in the frequencydistribution of the motion vectors, and an evaluation standard of thechange amount of the frequency amount.

The image evaluation portion may update the model frequency distributionof the motion vectors set beforehand as an evaluation standard of apreferable image, based on a trend of the frequency distribution of themotion vectors of an image selected by a user, from among the imagessubjected to the evaluation.

The image evaluation portion may update the dispersion of the modelfrequency distribution of the motion vectors set beforehand as theevaluation standard of a preferable image, based on a trend in thedispersion of the frequency distribution of the motion vectors of animage selected by a user, among the images subjected to the evaluation.

The image evaluation portion may update a frequency amount of a peakfrequency included in the model frequency distribution, used whendefining the model frequency distribution of the motion vectors setbeforehand as the evaluation standard, based on a frequency amount of apeak frequency included in the frequency distribution of the motionvectors of an image selected by the user, among the images subjected tothe evaluation.

The image evaluation portion may update the threshold frequency whenextracting the frequency distribution of the motion vectors formed bythe peak frequency having a frequency amount greater than the thresholdfrequency, based on the frequency amount of the peak frequency includedin the frequency distribution of the motion vectors of an image selectedby a user from among the images which have been subjected to theevaluation.

The image evaluation portion may update the evaluation standard based onthe change amount of the frequency amount of the peak frequency includedin the frequency distribution of the motion vectors of an image selectedby a user, from among the images subjected to the evaluation.

The image evaluation portion may comprise a system which evaluates oneimage using a plurality of evaluation methods which differ from eachother, and for a plurality of the images which are subjects of theevaluation, for the case of a predetermined plurality of continuousimages having higher evaluations by a same evaluation method thanevaluations by the other evaluation methods, the evaluation with respectto the plurality of the images is carried out such that the evaluationaccording to the same evaluation method is higher than the evaluation bythe other evaluation methods.

The image evaluation portion may comprise a system which evaluates oneimage using a plurality of evaluation methods which differ from eachother, and an evaluation with respect to the plurality of the images iscarried out such that an evaluation by an evaluation method whichassigns a higher evaluation than the other evaluation methods, andfurther which has the highest percentage of high evaluations withrespect to a plurality of the images among the plurality of evaluationmethods, is made to have a relatively higher evaluation than theevaluations by the other evaluation methods.

The image evaluation portion may comprise a system which evaluates oneimage using a plurality of evaluation methods which differ from eachother, and for a plurality of the images subjected to the evaluation, anevaluation method with a high evaluation with respect to a selectedimage for which a selection indication was made by a user, is made tohave a relatively higher evaluation than the other evaluation methods,and the evaluation of the images other than the selected image iscarried out.

The image evaluation portion may store in the storage portion evaluationresults of an image, added as additional information to the image whichwas subjected to evaluation, and the image evaluation portion may selectat least one image having evaluation results which are equal to orgreater than a predetermined threshold value from among the plurality ofimages stored in the storage portion, correspondingly stores in thestorage portion the image and its evaluation results, and also deletesfrom the storage portion the other images and the evaluation resultscorresponding to the other images.

The image evaluation apparatus may comprise: an imaging conditioncorrection portion which, in the case that there is an evaluation itemrequiring correction among the evaluation results of a plurality ofimages stored in the storage portion, carries out control so that thecondition is corrected when imaging with the camera based on theevaluation item.

The image evaluation apparatus may comprise: a display portion whichdisplays at least an image stored in the storage portion and correctioninformation relating to the image, and a correction informationextraction portion which analyzes evaluation results of an image storedin the storage portion and extracts correction information in order toincrease the evaluation results of the image, and wherein the imageevaluation portion display on the display portion an image stored in thestorage portion and correction information of the image extracted by thecorrection information extraction portion.

The image evaluation portion may comprise a system which evaluates oneimage using a plurality of evaluation methods which differ from eachother, and carry out respective evaluations equally with the pluralityof evaluation methods with respect to images subjected to evaluation,among the plurality of continuous images, until predetermined conditionsare satisfied, and carry out evaluations with weightings with respect tospecified evaluation methods among the plurality of the evaluationmethods, with respect to an image subjected to evaluation after thepredetermined conditions have been satisfied, and the image evaluationportion may further judge that the predetermined evaluation conditionshave been satisfied when an image is arbitrarily selected from among theplurality of images, or continuity is manifested in the evaluationresults of the plurality of evaluation methods with respect to aplurality of the images.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block drawing showing the functional constitution of thecamera according to embodiment 1;

FIG. 2 is a flowchart showing the procedures from photographing tostoring of the image in embodiment 1;

FIG. 3A is conceptual drawings for explaining the changes in the motionvectors;

FIG. 3B is conceptual drawings for explaining the changes in the motionvectors;

FIG. 4A is conceptual drawings showing histograms of the motion vectors;

FIG. 4B is conceptual drawings showing histograms of the motion vectors;

FIG. 5 is a conceptual drawing showing histograms of the defocus;

FIG. 6 is a conceptual drawing for explaining a change in the defocusamount;

FIG. 7 is a graph showing the time variation of the relative position ofthe subject in the optical axis direction;

FIG. 8A is conceptual drawings for explaining changes in the motionvectors of the subject;

FIG. 8B is conceptual drawings for explaining changes in the motionvectors of the subject;

FIG. 9 is a conceptual drawing for explaining changes in the histogramof the motion vectors;

FIG. 10 is a graph for explaining changes in the number of peakfrequencies included in the frequency distribution of the motion vector;

FIG. 11 is a conceptual drawing for explaining the histogram of themotion vectors of the imaged image;

FIG. 12A is conceptual drawings for the case where the histogram of FIG.10 has been made two dimensional;

FIG. 12B is conceptual drawings for the case where the histogram of FIG.10 has been made two dimensional;

FIG. 13A is a histogram showing the model frequency distribution of themotion vectors in the model image;

FIG. 13B is a histogram showing the frequency distribution of the motionvectors in the imaged image;

FIG. 14A is a conceptual drawing for the case that the histogram shownin FIG. 13A has been made two dimensional;

FIG. 14B is a conceptual drawing for the case that the histogram shownin FIG. 13B has been made two dimensional;

FIG. 15A is explanatory drawings showing examples of images which havebeen highly evaluated in an evaluation using the frequency distributionof the model image;

FIG. 15B is explanatory drawings showing examples of images which havebeen highly evaluated in an evaluation using the frequency distributionof the model image;

FIG. 15C is explanatory drawings showing examples of images which havebeen highly evaluated in an evaluation using the frequency distributionof the model image;

FIG. 16A is explanatory drawings showing examples of images which havebeen highly evaluated in an evaluation using the frequency distributionof the model image;

FIG. 16B is explanatory drawings showing examples of images which havebeen highly evaluated in an evaluation using the frequency distributionof the model image;

FIG. 17A is a conceptual drawing showing the frequency distribution ofthe motion vectors and its dispersion in the imaged image;

FIG. 17B is a conceptual drawing showing the model frequencydistribution and its dispersion in the model image;

FIG. 18A is histograms showing the frequency distribution of the motionvectors in the images continuously imaged by the camera;

FIG. 18B is histograms showing the frequency distribution of the motionvectors in the images continuously imaged by the camera;

FIG. 19A is a histogram showing the model frequency distribution of themotion vectors in the model image;

FIG. 19B is a histogram showing the frequency distribution of the motionvectors in the image which has been image-selected by the user;

FIG. 19C is a histogram showing the model frequency distribution of themotion vectors in the updated model image;

FIG. 20A is a conceptual drawing of the histogram showing the modelfrequency distribution of the motion vectors in the model image;

FIG. 20B is a conceptual drawing of the histogram showing the frequencydistribution of the motion vectors in the image which has beenimage-selected by the user;

FIG. 20C is a conceptual drawing of the histogram of the model frequencydistribution of the motion vector in the model image with the updateddispersion;

FIG. 21A is a conceptual drawing of the histogram showing the modelfrequency distribution of the motion vectors in the model image;

FIG. 21B is a conceptual drawing of the histogram showing the frequencydistribution of the motion vectors in the image which has beenimage-selected by the user;

FIG. 21C is a conceptual drawing of a histogram showing the modelfrequency distribution of the motion vectors in the model image with theupdated peak frequency;

FIG. 22 is a conceptual drawing showing the imaged image and itsevaluation results;

FIG. 23 is a conceptual drawing showing the imaged image and itsevaluation results;

FIG. 24 is a flowchart showing the procedure for the case of changingthe weighting of the evaluation method based on the image selected bythe user in embodiment 1;

FIG. 25 is a conceptual drawing showing one example of the imageevaluation screen displayed on the display panel;

FIG. 26 is a block diagram showing the functional constitution of thecamera according to embodiment 2;

FIG. 27 is a flowchart showing the procedures from photographing tostoring of the image in embodiment 2;

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the image evaluation apparatus and camera according tothe present invention are described below with reference to thedrawings. In the present embodiments, an explanation is given concerninga camera provided with the image evaluation apparatus according to thepresent invention. Embodiment 1 shows an example of the image evaluationapparatus according to the present invention applied to aninterchangeable lens type camera.

In the explanation below, “threshold value” is a standard value usedwhen carrying out various judgments. The threshold value is a valuedetermined beforehand as a characteristic value of the camera. Thethreshold value may be a fixed value. Alternatively, it may be avariable changed automatically or by a user setting.

1. Embodiment 1

1.1 Constitution of the Camera

FIG. 1 is a block diagram showing the functional constitution of thecamera according to embodiment 1. The camera 1 of embodiment 1 isprovided with a camera body 2 and a lens barrel 3. The camera 1 isconstituted as an interchangeable lens type camera wherein a lens barrel3 is mountable on and dismountable from the camera body 2.

The camera body 2 is provided with a main mirror 4, a sub-mirror 5, ashutter 6, an imaging sensor 7, a finder screen 8, a pentaprism 9, aphotometric lens 10, a photometric sensor 11, and an eyepiece lens 12 asoptical system components.

The subject light is admitted through the photographic lens 13 of thelens barrel 3. One part of the subject light (below referred to as themain mirror light) is reflected upwards at the main mirror 4. The mainmirror light is imaged at the finder screen 8. The main mirror lightimaged on the finder screen 8 is viewed by the user (photographer) viathe pentaprism 9 and eyepiece lens 12.

The main mirror light imaged at the finder screen 8 is also guided tothe photometric sensor 11 via the pentaprism 9 and the photometric lens10.

The photometric sensor 11 is constituted of a color imaging sensor suchas a CCD or a CMOS. The photometric sensor 11 divides the subject imageinto a plurality of regions and measures the light. The photometricsensor 11 commences its operation when the user half presses the releasebutton, not illustrated. The photometric sensor 11 detects the image ofthe picture imaged on the finder screen 8 as image information. Theimage information detected by the photometric sensor 11 is transmittedto the body side microcomputer 21.

Moreover, in the present embodiment, the image of the picture detectedby the photometric sensor 11 is referred to as the “image information”.Further, the image data prepared by the later described image processingportion 24 is referred to as the “image”. The image information detectedby the photometric sensor 11 is originally obtained for photometricinformation color information used for exposure computation.

The main mirror 4 is provided with a half mirror in the vicinity of itscenter. The subject light transmitted by this half mirror (belowreferred to as half mirror light) is reflected at the sub-mirror 5provided at the rear side of the main mirror 4. Then, the half mirrorlight is guided to the focus detection portion 22 disposed at the lowerportion of the camera body 2. The main mirror 4 and the sub-mirror 5 aredriven in the vertical direction by the mirror driving mechanism, notillustrated. The mirror driving mechanism is controlled by the body sidemicrocomputer 21.

When the user fully presses the release button, the main mirror 4 isflipped upwards along with the sub-mirror 5 (mirror up) by the mirrordriving mechanism. Next, when the shutter 6 opens, the subject light isimaged on the light receiving face of the imaging sensor 7. The imagingsensor 7 is constituted of a photodiode as well as a CCD or CMOS or thelike. The imaging sensor 7 receives the subject image imaged by thephotographic lens on its light receiving face, converts it to anelectric signal (image signal), and outputs this to the image processingportion 24. After this, the main mirror 4 along with the sub-mirror 5are returned down by the mirror driving mechanism (mirror down).

The imaging sensor 7 in the present embodiment functions as an imagingportion which continuously images a plurality of frame images of oneframe of the subject in the later described photographic frame atpredetermined time intervals.

The camera body 2 is further provided with a body side microcomputer 21,a focus detection portion 22, a shutter control portion 23, an imageprocessing portion 24, a buffer 25, an I/F (interface) portion 27, adisplay panel 28 and an operating portion, not illustrated, ascomponents of a control system.

The body side microcomputer 21 controls the operation of the camera 1overall including the lens barrel 3. The body side microcomputer 21 isconstituted of a microprocessor. The body side microcomputer 21 computesthe lens driving amount based on the defocus information (amount)transmitted from the focus detection portion 22. Then, the body sidemicrocomputer 21 transmits the lens driving amount to the laterdescribed lens side microcomputer 18.

Further, the body side microcomputer 21 computes a suitable exposurevalue based on the image signal from the imaging sensor 7, the apertureF value of the photographic lens 13 stored in the lens sidemicrocomputer 18, lens information such as the focal length and thelike, the set sensitivity information of the imaging sensor 7 and thelike. Then, the body side microcomputer 21 transmits the aperture valuedetermined based on the computed suitable exposure value to the lensside microcomputer 18. Further, the body side microcomputer 21 transmitsthe shutter speed determined based on the computed suitable exposurevalue to the shutter control portion 23.

In the camera 1 of the present embodiment, the detection of imageinformation by the photometric sensor 11 and the detection of defocusinformation by the focus detection portion 22 are carried out atpredetermined time intervals during the half press of the releasebutton, specifically at a frame rate of 30 fps (30 frames per onesecond). This information is sequentially stored in the buffer 25.

The body side microcomputer 21 also carries out the flipping up of themain mirror 4 and the sub-mirror 5 when the release button, notillustrated, is fully pressed. The body side microcomputer 21 alsodrives the shutter control portion 23 and opens and closes the shutter6.

The body side microcomputer 21 in the present embodiment, besides theabove processing, also executes processes as the later described imageevaluation portion, photographic condition correction portion, andcorrection information extraction portion.

The focus detection portion 22 detects the defocus information based onthe admitted subject light. The defocus information can detect using,for example, the phase difference detection method. The focus detectionportion 22 of the present embodiment detects the focus position of thesubject relative to a plurality of ranging areas inside the picture, andtransmits the defocus amount at this time to the body side microcomputer21 as defocus information. Moreover, focus adjustment is usually carriedout based on defocus information detected for one (or a plurality of)ranging area(s) among a plurality of ranging areas. However, defocusinformation detected for the other ranging areas is also output to thebody side microcomputer 21.

The shutter control portion 23 controls the opening and closing of theshutter 6. The shutter control portion 23 is controlled by the body sidemicrocomputer 21.

The image processing portion 24 carries out as necessary processing fornoise elimination, A/D conversion, color interpolation processing, sizeconversion, encoding and the like, for the image signal output from theimaging sensor 7, and generates the image data (image) of each frame.

The buffer 25 is a temporary storage portion which stores the imagegenerated by the image processing portion 24, and the histogram showingthe frequency distribution of the motion vectors. The buffer 25 isprovided with a photographed image storage area which stores the image,and a histogram storage area which stores the histogram showing thefrequency distribution of the motion vectors. The buffer 25 of thepresent embodiment functions as a storage portion storing the imageimaged by the imaging sensor 7 which is the imaging portion.

The storage portion 26 is a storage portion which holds its storedcontent even when the power of the camera 1 is switched off. The storageportion 26, besides the storage of a variety of setting information,also stores the later described model frequency distribution of themotion vectors in the model image and the like.

The I/F portion 27 is provided with a function of recording to thememory card 20 the image stored in the buffer 25, and the function ofreading out the image recorded in the memory card 20, and is awrite/read apparatus. A memory card 20 is removably mounted in thememory card slot, not illustrated, of the I/F portion 27.

The display panel 28 is constituted of a color liquid crystal panel. Thedisplay panel 28 is disposed at the back face of the camera body 2. Thedisplay panel 28, in addition to displaying an image during photographyor an image stored in the buffer 25, displays a variety of informationrelating to the image. The display panel 28 in the present embodimentfunctions as a display portion displaying the image stored in the buffer25, and the correction information related to this image.

The camera 1 is provided with an operating portion, not illustrated.This operating portion is an input acquisition portion which acquiresoperations by the user. The operating portion is constituted of theabove mentioned release button, and a variety of dials, buttons, leversand the like.

Next, an explanation is given concerning the lens barrel 3. The lensbarrel 3 is provided with a photographic lens 13, an aperture unit 14, alens driving portion 15, a distance detection portion 16, an aperturecontrol portion 17, and a lens side microcomputer 18.

The photographic lens 13 is an optical component which refracts theadmitted subject light, and forms an image of the subject image at thelight receiving face of the imaging sensor 7 at its exit side. Thephotographic lens 13 is driven along the optical axis direction by thelens driving portion 15. Focus adjustment is carried out by thisdriving. When the focus adjustment is carried out, distance informationshowing the distance between the camera 1 and the subject is detected bythe distance detection portion 16.

The aperture unit 14 drives the aperture blades, not illustrated,according to the aperture value set by the lens side microcomputer 18.An area through which the subject light passes is formed by the drivingof the aperture blades. The aperture unit 14 is driven by the aperturecontrol portion 17.

The lens side microcomputer 18 controls the lens driving and theaperture value. The lens side microcomputer 18 is constituted of amicroprocessor. The lens side microcomputer 18 acquires the distanceinformation at the time the photographic lens 13 is driven, from thedistance detection portion 16, and transmits it to the body sidemicrocomputer 21. The lens side microcomputer 18 controls the lensdriving portion 15 based on the lens driving amount transmitted from thebody side microcomputer 21. Further, the lens side microcomputer 18controls the aperture control portion 17 based on the aperture valuetransmitted from the body side microcomputer 21.

The exchange of signals between the body side microcomputer 21 and thelens side microcomputer 18 is carried out via the connection portion 19disposed between the camera body 2 and the lens barrel 3. Thisconnection portion 19 is provided at the respective mount portions ofthe lens barrel 3 and the camera body 2. The connection portion 19 iselectric contacts which make contact and electrically transmit when thelens barrel 3 is mounted on the camera body 2.

1.2 Imaging Procedure and Operation

Next, an explanation is given referring to the flowchart of FIG. 2 forthe series of procedures from the photographing to the storage of theimage in the camera 1 of embodiment 1 constituted as described above.The processing of the flowchart shown in FIG. 2 is executed by the bodyside microcomputer 21. The processing based on this flowchart is startedwhen the user selects the image evaluation mode item from the settingscreen, not illustrated, and half presses the release button, notillustrated.

In step S101, the photometric sensor 11 acquires the image of thepicture imaged on the finder screen 8 as image information. Thephotometric sensor 11 stores the acquired image information in thephotographed image storage area of the buffer 25. The photometric sensor11 acquires the image information at a frame rate of 30 fps, forexample.

Moreover, in the case that the camera 1 is provided with a live viewfunction, the image data (image) of the live view picture imaged by theimaging sensor 7 may be stored in the photographed image storage area ofthe buffer 25.

In step S102, the focus detection portion 22 acquires the defocusinformation corresponding to the plurality of ranging areas in thepicture. Then, the focus detection portion 22 transmits the acquireddefocus information to the body side microcomputer 21.

In step S103, the body side microcomputer 21 carries out the photometriccomputation (1), the motion vector computation (2), and thepreprocessing computation for the main subject position estimation (3),as described below using the image information acquired in step S101.

In the photometric computation (1), the body side microcomputer 21segments the image information of one picture portion into a pluralityof blocks (segmented regions). Then, the body side microcomputer 21calculates the luminance information (luminance value) of the subjectfrom the output value of the segmented image information, as well as thephotometric sensor gain and exposure time.

In the motion vector computation (2), the body side microcomputer 21detects the correlation value between the image information of theprevious acquisition, and the image information of the currentacquisition, for the image information acquired at a frame rate, forexample of 30 fps. FIG. 3A and FIG. 3B are conceptual drawings forexplaining the changes in the motion vectors. As shown in the figures,the picture (image frame) 101 is segmented into a plurality of blocks.In the computation of the motion vector, the body side microcomputer 21examines by a method of pattern matching to which position in thecurrently acquired frame shown in FIG. 3B the image in the previouslyacquired frame shown in FIG. 3A has moved. In this way, the body sidemicrocomputer 21 acquires the motion vector of the subject for eachblock.

In the pattern matching, the body side microcomputer 21, for each of thesegmented blocks, inspects whether it fits a specified pattern, or towhat degree there is resemblance. The more it fits with the specifiedpattern, or the more the resemblance, the higher the correlation value.The motion vector is detected as information for each block. Moreover,the motion vector may be sought by a method other than pattern matching,and may be sought by a combination of a plurality of methods includingpattern matching.

In the preprocessing computation for the main subject positionestimation (3), the body side microcomputer 21 detects the position ofwhat is estimated to be a person's face within the image acquired as theimage information. This face position detection uses a database storinga plurality of pattern images having common features of faces. The bodyside microcomputer 21 seeks portions resembling the pattern imagesstored in the database, in the target image, by a pattern matchingmethod. When there is a portion which resembles a pattern image, thisportion is estimated to be a face. The region of the portion estimatedto be a face by pattern matching is used for the later described mainsubject position estimation computation as the face detection regioninformation. Moreover, the region may be said to be in a range specifiedfrom one block or a plurality of blocks.

Moreover, also in this case, face detection may be sought by a methodother than pattern matching, and it may be sought by a combination of aplurality of methods including pattern matching. In this case, a judgingmethod may also be considered where the value obtained by each method isassigned a score, and the portion with the highest score is the mainsubject, or the like.

Further, as the preprocessing computation for the main subject positionestimation (3), besides obtaining the above described face detectionregion information, it is possible to use a method obtainingdistribution information of the colors from within the imageinformation. This method is a method which, along with detecting a valueof the output ratio R/G, B/G for each color, for each pixel or eachsegmented block of the image information, also detects this value at theperiphery of the picture, and judges whether or not it is close to thevalue detected at the picture periphery in each block in the picture.According to this method, if there is a region within the picture whosecolors differ greatly from the periphery of the picture, it is judgedthat there is a high probability that this is the main subject becausethis region is a subject which differs from the background. Theinformation relating to this region can be used for the later describedmain subject position estimation computation.

In Step S104, the body side microcomputer 21 computes the defocusinformation transmitted from the focus detection portion 22 as focusdetection information. Further, the body side microcomputer 21 selectsthe focus detection point based on the focus detection informationcomputed from the defocus information acquired in a prescribed period.Namely, the body side microcomputer 21 selects the focus detection pointshowing the depth direction position which is closest to the depthdirection position of the subject acquired the previous time, from amongthe focus detection information computed from the defocus informationacquired the current time.

By doing this, the body side microcomputer 21 captures and follows thephotographed subject, and can obtain information concerning where thesubject, in the frame acquired at the current time, is distributed inthe planar direction in the picture. At this time, if the distance ring(focus ring) of the photographic lens 13 is driven during one cycle, thebody side microcomputer 21 can correct the defocus information accordingto the amount of movement in its peripheral direction, and correct thedepth direction position of the subject in a virtual space.

Further, the body side microcomputer 21, in the case of estimating thatthe subject is moving from the defocus information, corrects the amountmoved by the subject during the period of the previous and current focusdetection, and acquires the position in the depth direction of thesubject.

In Step S105, the body side microcomputer 21 carries out processing asthe image evaluation portion. In this processing, the body sidemicrocomputer 21 evaluates the image information stored in thephotographed image storage area of the buffer 25 based on acharacteristic amount which changes in a chronological sequence in thephotographic frame when images of a plurality of frames are imaged.Specifically, the body side microcomputer 21 calculates the frequencydistribution of the motion vectors sought in step 103 concerning theimage information stored in the buffer 25, and evaluates thecharacteristic amount of this frequency distribution.

Next, the frequency distribution of the motion vectors is specificallyexplained. FIG. 4 is a histogram showing the frequency distribution ofthe motion vectors of the imaged image. In FIGS. 4A and 4B, the twohorizontal axes express the motion vector amounts (grades) showing thelevels of the motion vectors in the X and Y directions in the picture,and the vertical axis represents the frequency. In the belowexplanation, the portions of the conical shapes where many frequenciesof motion vectors are concentrated are referred to as “peak frequencyincluded in the frequency distribution of the motion vectors” or “peakfrequency”.

FIG. 4A is a histogram showing the frequency distribution of the motionvectors in an automobile race. In an automobile race, if the subject isone automobile, it is estimated that the smaller the number of peakfrequencies, the larger the automobile which is the subject of thepicture will be shown. Accordingly, in this case, when an image has asmaller number of peak frequencies, it will be a more highly evaluatedimage, and a higher number of points will be added.

Further, even though also a sport, when the photographic scene issoccer, the evaluation is different. FIG. 4B is a histogram showing thefrequency distribution of the motion vectors in soccer. In a sportplayed by a large number of players such as soccer, it is estimated thatthe higher the number of frequency peaks, the greater the number ofplayers present in the picture, and the livelier the scene. Accordingly,the body side microcomputer 21 gives a high evaluation to an image witha high number of peaks, and a high number of points will be added.

In this way, the evaluation standard of an image differs depending onthe photographed scene. Because of this, for example, the evaluationstandard changes in response to the set photographic scene, and theevaluation can be carried out to match the user's intentions by changingthe evaluation standard by a user setting. Moreover, the evaluationmethod is not limited to the above examples, and other evaluationmethods may be applied. Other evaluation methods will be explainedlater.

The body side microcomputer 21 generates a histogram of the motionvectors such as shown in FIG. 4. Then, the body side microcomputer 21adds a number of points which becomes the evaluation result to the imagecorresponding to the number of peak frequencies. The body sidemicrocomputer 21 stores the generated histogram in the histogram storagearea of the buffer 25 in association with the image information. Thephotographed image and its evaluation result are stored in thephotographed image storage area of the buffer 25 as described later.

Further, when using a telephoto lens or a micro lens, it can beconsidered that the subject will often be photographed as a close-up.Because of this, the body side microcomputer 21 will give a highevaluation to an image with a small number of peak frequencies, and adda high number of points, when using a telephoto lens or a micro lens.Further, when a mid-telephoto to wide angle lens is used, it can beconsidered that the subject will often be photographed at wide range.Because of this, the body side microcomputer 21 will give a highevaluation to an image with a high number of peak frequencies, and add ahigh number of points, when using a mid-telephoto to wide angle lens. Inthis way, the evaluation standard may be automatically changed inresponse to the focal distance of the photographic lens which ismounted. In the case of changing the evaluation standard in response tothe focal length of the photographic lens, it is not necessary for auser to make this setting every time. Because of this, the operabilityof the camera can be improved.

Moreover, in the case that a high number above a predetermined number ofpeaks is generated by the motion vectors distributed in the picture, thepeak values which do not meet a threshold value may be excluded asobjects of the evaluation. In this way, it is possible to increase thespeed of the evaluation processing of an image.

Further, the body side microcomputer 21, as a process of the imageevaluation portion, may also evaluate the image information stored inthe buffer 25 as the characteristic amount of the defocus informationsought in Step S104. In this case, the body side microcomputer 21generates a histogram of the defocus. FIG. 5 is a conceptual drawingshowing the histogram of the defocus. In FIG. 5, the horizontal axis (Xaxis) represents the difference between frames of the defocus amount (Dfdifference), and the vertical axis (Y axis) represents the frequency.The body side microcomputer 21 seeks the difference in the defocusamount for each block between frames, and generates a histogram of thesedifferences.

The body side microcomputer 21 generates a histogram of the defocus asmentioned above, and adds points to what will become the evaluationresults of the image, corresponding to the number of peak frequencies.In the case that the defocus is taken as the characteristic value, animage where a change is taking place within the picture is taken as agood image, and a high number of points is added to an image having ahigh number of histogram peaks. Further, points may be given to an imagehaving a peak at or above a threshold value.

Moreover, the defocus information of each image is stored along with theimage information in the image file (for example, the EXIF format imagefile). This image file is stored in the memory card 20.

It is also possible to evaluate using a characteristic amount of acombination of the motion vector and the defocus information. Forexample, investigating the defocus amount at the respective peaks of themotion vectors, or investigating the motion vectors at the respectivepeaks of defocus amount. Alternatively, as a comprehensive evaluation,an average of the points of the respective weighted evaluation resultsmay be taken as the evaluation result. In this way, a more detailedevaluation can be carried out.

The body side microcomputer 21, as a process of the image evaluationportion, after carrying out the evaluation for the target image, adds asadditional information the evaluation result of the image to theevaluated image, and stores this in the photographed image storage areaof the buffer 25.

In Step S106, the body side microcomputer 21, as a process of the imageevaluation portion, carries out the main subject position estimationcomputation for the image for which the evaluation of Step S105 wascarried out. In the present embodiment, detection of the face detectionregion information is carried out. Specifically, the body sidemicrocomputer 21 segments the picture of the target image into aplurality of blocks and compares this image with the face detectionregion information obtained in Step S103 for the image in question.Then, the body side microcomputer 21 adds a predetermined number ofpoints to the region where a face image is detected.

In the present embodiment, as the main subject position estimationcomputation, an explanation is given for an example using the facedetection region information sought in Step S103. However, it is alsopossible to use other parameters for the main subject positionestimation computation. For example, in the case of using the motionvector sought in Step S103, a predetermined number of points is added toa region with a high probability of having the main subject (forexample, a ranging area indicated by the user and the region of itsvicinity; or a region in the vicinity of the center of the frame in thecase of no indication by the user; or the like). Further, in the case ofusing the color distribution information explained in Step S103, in thesame way, a predetermined number of points is added to a region having ahigh probability of having the main subject. Further, in the case ofusing the focus detection information sought in Step S104, apredetermined number of points is added to a region where a subject isdistributed close to the position in the depth direction of the capturedsubject.

Further, in the main subject position estimation computation, all of theabove computation items may be combined together. In this case, thecontribution for each item may be adjusted by appropriately setting thesize of the number of points to be added for each item.

In Step S107, the body side microcomputer 21, based on the position ofthe main subject sought in Step S106, and the luminance informationcalculated in Step S103, carries out the exposure computation takinginto account the luminance of the main subject. The body sidemicrocomputer 21 determines the aperture value and the shutter speedbased on the suitable exposure value sought by the exposure computation.Then, the body side microcomputer 21 transmits the aperture value to thelens side microcomputer 18, and transmits the shutter speed to theshutter control portion 23. The lens side microcomputer 18 carries outthe aperture adjustment controlling the aperture control portion 17based on the aperture value transmitted from the body side microcomputer21. The shutter control portion 23 controls the shutter 6 based on theshutter speed transmitted from the body side microcomputer 21. In thisway, the shutter 6 is opened and closed.

The body side microcomputer 21, along with selecting defocus informationcorresponding to the main subject from the defocus information sought inStep S104, based on this also computes the driving amount of thephotographic lens 13 and transmits the same to the lens sidemicrocomputer 18. The lens side microcomputer 18 controls the lensdriving portion 15 based on the lens driving amount transmitted from thebody side microcomputer 21 and carries out the focus adjustment of thephotographic lens 13 by driving the distance ring.

In Step S108, the body side microcomputer 21, as a process of the imageevaluation portion, evaluates the image information stored in the buffer25 based on the characteristic amount of the motion vector and thedefocus amount and the like, and the position of the main subject soughtin Step S106.

In this Step S108, the comprehensive evaluation of the photographedimage is carried out by totaling the points given when evaluating themotion vector and the defocus amount as the characteristic amounts, andthe points given when evaluating based on the later described positionof the main subject.

As the evaluation based on the position of the main subject, forexample, there are the items of the evaluation of the focus/blurring asbelow, the evaluation of the white balance, evaluation of the exposureand the like. The body side microcomputer 21 adds points to execute theevaluation for at least one among these items. The body sidemicrocomputer 21, in the case that evaluation has been carried out for aplurality of items, seeks a total or average of the points for eachitem.

The body side microcomputer 21, in the focus/blurring evaluation,applies a differential filter to the photographed image and detects theedges. Then, the body side microcomputer 21 detects from a half valuewidth whether the edges are wide or narrow, and portions narrower than apredetermined value are judged to be in focus. In this way, the bodyside microcomputer 21 judges whether or not a portion which is in focusis at the position of the main subject sought in Step S106. The bodyside microcomputer 21 judges that the focus is good if a portion whichis in focus is included at the position of the main subject sought inStep S106, and adds a predetermined number of points.

Further, the body side microcomputer 21 can specify a region where thesubject is distributed more towards the side of infinite distance thanthe position of the main subject, and a region where the subject isdistributed at the near side, from the defocus information obtained inStep S102. The body side microcomputer 21 judges that an image has backfocus if the portion which is in focus is distributed more towards theinfinite distance side than the position of the main subject. Further,the body side microcomputer 21 judges that the image has front focus ifthe portion which is in focus is more towards the near side than theposition of the main subject.

On the other hand, if there is no portion anywhere which is in focus,and the edge thickness is similar throughout the picture, there is ahigh probability that the image is blurred by vibrations. In this case,the body side microcomputer 21 judges that vibrations are arising.

In this way, points are not added for the case that the focus is backfocused or front focused, or the case of vibrations.

In the case of white balance evaluation, the body side microcomputer 21first calculates the color coordinates at the position of the mainsubject estimated to be a face. Then, the body side microcomputer 21judges whether the calculated color coordinates are within the scope ofcolor coordinates which are expected (thought to be preferable) for ahuman face. The body side microcomputer 21 judges that the white balanceis not a problem with respect to persons if the color coordinates of themain subject are within the scope of the expected color coordinates, andadds a predetermined number of points.

The exposure evaluation is carried out for the items below. The bodyside microcomputer 21 judges whether there is underexposure (blackout)or overexposure (whiteout) at the position of the main subject estimatedto be a face. Further, the body side microcomputer 21 seeks regionswhere there is underexposure (blackout) or overexposure (whiteout) withrespect to the overall picture, and judges whether their proportion isbelow a predetermined value. Further, the body side microcomputer 21judges whether it is within a preferable density scope of a face at themain subject position. The body side microcomputer 21 considers thatthere is no problem with the exposure if there judgments are allpositive, and adds a predetermined number of points.

Moreover, in the case that a plurality of items are the evaluationtargets, the contribution ratio of each item can be adjusted byappropriately setting the number of points added for each item. Further,the contribution ratios may be such that they can be individually set bya setting screen, not illustrated, according to the importance given bythe user. In this case, the body side microcomputer 21 adjusts theissuance ratio for each item by differentiating the numbers of pointsfor each item according to the conditions set through the settingscreen.

The body side microcomputer 21 carries out the comprehensive evaluationof the photographed image by calculating a total value or average valueof the points from the evaluations based on the position of the mainsubject described above, and the points from the evaluation of themotion vector or the defocus amount or the like as the characteristicamount in the previous Step S105.

In Step S109, the body side microcomputer 21 judges whether or not therelease button, not illustrated, is fully pressed. In the judgment ofStep S109, if it is not being fully pressed, the flow proceeds to StepS115. Further, if it is being fully pressed, the flow proceeds to StepS110. In Step S110, the body side microcomputer 21 puts the main mirror4 along with the sub-mirror 5 in a mirror up state.

In Step S111, the body side microcomputer 21 drives the shutter controlportion 23 and opens and closes the shutter 6, and images (exposes) animage on the imaging sensor 7. The image imaged on the imaging sensor 7is output to the image processing portion 24 as an image signal.

In Step S112, the body side microcomputer 21 controls the mirror drivingmechanism, not illustrated, and puts the main mirror 4 along with thesub-mirror 5 in a mirror down state. In this way, the main mirror 4 andthe sub-mirror 5 are returned to the position shown in FIG. 1.

In Step S113, the body side microcomputer 21 takes the evaluationresults obtained in Step S108 immediately before the release button wasfully pressed as the evaluation results of the image photographed justafter the release button was fully pressed. In Step S114, the body sidemicrocomputer 21 stores the photographed image and its evaluationresults in the photographed image storage area of the buffer 25.

The buffer 25 stores in respective predetermined areas the imageinformation acquired by the photometric sensor 11 during the half pressof the release button, the evaluation results of the image by this imageinformation, and the information concerning the histogram. The body sidemicrocomputer 21 stores the image photographed when the release buttonwas fully pressed (or during the full press) and its evaluation resultsin the photographed image storage area of the buffer 25. After this, thebody side microcomputer 21 erases each type of information such as theimage information acquired by the photometric sensor 11 and the likefrom the predetermined areas of the buffer 25.

In Step S115, the body side microcomputer 21 judges whether the releasebutton is being fully pressed. If the release button is not being fullypressed in the judgment of Step S115, the processing of the presentflowchart is finished. On the other hand, if it is being fully pressedin the judgment of Step S115, the flow returns to Step S101.

The camera 1 repeatedly carries out the processes of Steps S101 to S114while the release button is being fully pressed. While the releasebutton is being fully pressed, images are continuously imaged at apredetermined imaging timing (for example, 2 to 9 frames/second). Thebody side microcomputer 21 does not record to the buffer 25 all of theimages imaged while the release button is fully pressed, and stores inthe buffer 25 only the images it has selected.

Namely, the body side microcomputer 21, as processing of the imageevaluation portion, selects at least one image whose image evaluationpoints are equal to or greater than a predetermined threshold value,from among the plurality of images stored in the photographed imagestorage area of the buffer 25 (images imaged by the imaging sensor 7).Then, this image and its evaluation results remain in the photographedimage storage area of the buffer 25. Furthermore, the other images andtheir evaluation results are deleted form the buffer 25. By carrying outsuch processing, the storage area of the buffer 25 can be effectivelyused.

All of the imaged images may also be stored in the buffer 25. Further,the evaluation may be carried out by extracting, for example, everyfifth image, and not evaluating all of the images.

The above described selection and storing of images is executed in thecontinuation of Step S116 of FIG. 2. However, in Step S114, when storingin the photographed image storage area of the buffer 25 the photographedimage and its evaluation results, it may be selected whether or not tostore based on the evaluation result points.

1.3 Other Evaluation Methods

In the above embodiment, an explanation was given for an example wherethe frequency distribution of the motion vectors is calculated, and theimage is evaluated with this frequency distribution as a characteristicamount. However, the evaluation method of the present invention is notlimited to this example, and an evaluation method such as those shown inthe below embodiments may also be applied.

1.3.1 Evaluation According to Change in Defocus Amount

FIG. 6 is a conceptual drawing for explaining a change in the defocusamount. FIG. 7 is a graph showing a change in time of the relativeposition of the subject in the optical axis direction. In the case ofphotographing a moving subject such as an automobile race, the change intime of the defocus information detected by the ranging area in thepicture is observed. As shown in FIG. 6, the vicinity of the center ofthe picture (photographic frame) 101 displays a focus zone 102.Pluralities of ranging areas, not illustrated, are disposed within thisfocus zone 102.

As explained before, usual focus adjustment is carried out based ondefocus information detected in one (or a plurality of) ranging areasamong a plurality of ranging areas. However, the defocus informationdetected for the other ranging areas is also output to the body sidemicrocomputer 21. In the present embodiment, the defocus amount of amain subject moving in the optical axis direction is detected in all ofthe ranging areas included in the ranging area relating to the focusadjustment. The body side microcomputer 21 divides the difference in thedefocus amount between frames by the frame rate (1/30 sec) andcalculates a speed change rate (relative movement state) of the mainsubject between the frames. When the main subject is moving at highspeed towards the camera 1, as shown in FIG. 7, the change in time ofthe relative position of the main subject becomes large. Accordingly,the speed change rate of the main subject also becomes large along withthe elapse of time.

The body side microcomputer 21 calculates a speed change rate for eachimage continuously photographed, and carries out evaluation of the speedchange amount as the characteristic amount. In an automobile race, animage photographed when an automobile which is the main subject isapproaching at high speed is assumed to be a good image having movement.Accordingly, the body side microcomputer 21 increases the evaluation ofthe image having the highest speed change ratio of the main subject, andadds a large number of points.

Further, requirements according to the composition may be added to thespeed change ratio. Namely, the evaluation may be carried out adding therequirements that the size of the main subject be equal to or greaterthan a threshold value in the whole of the picture 101, and that themain subject be in the vicinity of the center of the picture 101. Theposition and size of the main subject can be specified by the method ofthe main subject position estimation explained in item 1.2. Further, theposition and size of the main subject can be specified based on theposition of the ranging area where defocus information is detectedwithin the focus zone 102. In the case that the evaluation is carriedout with requirements according to the composition added to the speedchange ratio, it is possible to increase the evaluation of an imagewhere the main subject is approaching at high speed, and further, themain subject is photographed in the vicinity of the center of thescreen, and large.

1.3.2 Evaluation by Change in Number of Peaks

FIG. 8A and FIG. 8B are conceptual drawings for explaining a change inthe motion vectors of the main subject. FIG. 9 is a conceptual drawingfor explaining a change in the histogram of the motion vectors. FIG. 10is a graph for explaining a change in the number of peak frequenciesincluded in the frequency distribution of the motion vectors.

In this evaluation method, the number of peak frequencies included inthe frequency distribution of the motion vectors arising within thepicture is observed, and the evaluation of an image imaged within apredefined time from a change in the number of peak frequencies is givena relatively higher evaluation than other images. The number of peakfrequencies can be considered to be a number of subjects which aremaking different movements within the picture. In the case that thenumber of peak frequencies has changed at a given point in time, it isestimated that some kind of change has occurred in the subject. For theoccurrence of a change in the subject, it can be considered that thereis a high probability of good scenes having movement shortly thereafter.Accordingly, the body side microcomputer 21 gives a high evaluation toimages imaged within a predefined time from a change in the number ofpeak frequencies. Moreover, small peaks generated by noise and the likeare cut with a filter.

The body side microcomputer 21, as shown in FIG. 8, computes a frequencydistribution of the motion vectors arising between the frames of eachblock, for a picture 101 which has been segmented into a plurality ofblocks. Then, the body side microcomputer 21 extracts peak frequencieswhich have a frequency equal to or greater than a threshold frequencyfrom among the peak frequencies included in this frequency distribution.Furthermore, the body side microcomputer 21, along with calculating thenumber of extracted peak frequencies, observes changes in chronologicalsequence in the number of the peak frequencies of each image.

For example, from a state wherein the batter is in a batting posture asshown in FIG. 8A, the swing of the bat starts, as shown in FIG. 8B.Thereupon, as shown in FIG. 9, in consecutive images, in a histogramshowing the frequency distribution of the motion vectors, the number ofpeak frequencies changes abruptly. Accordingly, there is a highprobability of good scenes having movement shortly after themanifestation of such a change. Because of this, the body sidemicrocomputer 21, as shown in FIG. 10, gives a higher evaluation thanother images to images imaged within a predetermined time (period A inthe figure), and adds a number of points.

Further, the evaluation method of the present embodiment may be used toextract an image which is the target of evaluation. Namely, the bodyside microcomputer 21 extracts a plurality of images imaged within apredefined time from the manifestation of the above described change.Then, the body side microcomputer 21 adds a number of points to theimage with the highest number of peak frequencies in the histogram ofthe motion vectors, or the image including the largest peak frequency,from among the extracted images. Besides the present example, it is alsopossible combine the evaluation method of the present embodiment withother evaluation methods.

1.3.3 Evaluation Using Model Image Histogram (1)

FIG. 11 is a conceptual drawing for explaining the histogram of themotion vectors in an imaged image. FIG. 12A and FIG. 12B are conceptualdrawings for the case that the histogram of FIG. 10 is converted to twodimensions. Further, the histogram of the motion vectors, as shown inFIG. 4 and FIG. 9, is shown as a three dimensional figure. However, ifnecessary, the histogram of the motion vectors may be shown as a twodimensional figure as shown in FIG. 12. This is in order to facilitatethe explanation of the histogram.

In the evaluation method of the present embodiment, the evaluation iscarried out based on the similarity between the frequency distributionof the motion vectors of the imaged image, and a model frequencydistribution of motion vectors set beforehand as an evaluation standardof a preferable image (below referred to as a model image).

First, the body side microcomputer 21 computes a frequency distributionof the motion vectors arising between the frames of each block for thepicture segmented into a plurality of blocks, and generates a histogramshowing the frequency distribution of the motion vectors. Here, ahistogram showing the frequency distribution of the motion vectors asshown in FIG. 11 is generated. The histogram showing the frequencydistribution of these motion vectors may in some cases includecomponents of camera movement such as hand shake and the like.Accordingly, the body side microcomputer 21, as shown in FIG. 12,carries out processing to shift the point of origin of the histogram.

FIG. 12A is a histogram showing the frequency distribution of the motionvectors before the point of origin is shifted. In the picture, assumingthat the main subject is in the vicinity of the center, it can beconsidered that the peak frequency A which is equal to or above athreshold frequency and manifested on the coordinate axis of the Xdirection is a motion component of the camera 1. In the present example,it can be considered that there is a hand shake component in the Xdirection. Thus, as shown in FIG. 12B, the origin of the histogram isshifted to the center of the peak frequency A present on the coordinateaxis of the X direction. In FIG. 12B, the dotted line shows thecoordinate axis in the Y direction before the shift.

Because the motion components of the camera 1 change between frames, theposition of the peak frequency A also changes between frames, as shownby the chain double dashed line in FIG. 12B. The peak frequencies B to Dwhich express characteristics of the subject also fluctuate betweenframes because motion components of the camera are superposed thereon,but if the point of origin of the coordinates of the histogram isshifted such that the peak frequency A becomes the origin, then themotion components of the camera are deleted, and the motion of thesubject emerges and can be manifested on the histogram. Accordingly, thebody side microcomputer 21, as shown in FIG. 12B, specifies thefrequency distribution of the motion vectors including peak frequenciesB to D as the frequency distribution of the motion vectors of the imagedimage. In this way, it is possible to delete the motion components ofthe camera 1, namely, the motion components of the photographing area,by carrying out processing to shift the origin for the histogram of themotion vectors. By deleting the motion component of the photographingarea, it is possible to carry out more accurate evaluation.

Further, while not shown in FIG. 11, it can also be considered thatthere are motion components of the camera 1, namely, hand shakecomponents in the Y direction, in the peak frequencies manifested on thecoordinate axis of the Y direction. In the case that peak frequenciesequal to or greater than a threshold frequency are not manifested on thecoordinate axis of the X direction, when peak frequencies equal to orgreater than a threshold frequency are manifested on the coordinate axisof the Y direction, it is possible to delete the motion components ofthe photographing area by carrying out processing to shift the point oforigin as above.

The body side microcomputer 21 compares the frequency distribution ofthe motion vectors in the imaged image, and the model frequencydistribution of the motion vectors in the model image, and evaluates theimage based on their similarity. The body side microcomputer 21 judgesthe similarity for each of the sequentially imaged images. The body sidemicrocomputer 21 increases the evaluation of images having a frequencydistribution with a high similarity to the model frequency distributionof the motion vectors of the model image, and adds a large number ofpoints. Further, pluralities of model frequency distributions of themotion vectors in model images are stored beforehand in the storageportion 26. The body side microcomputer 21 reads out all of or a portionof the model frequency distributions of the motion vectors in the modelimages stored in the storage portion 26, selects a model frequencydistribution of the motion vectors in the model image which approximatesthe frequency distribution of the motion vectors in the imaged image,and carries out the evaluation of the image. Further, evaluation of animage may also be carried out by reading out and using a model frequencydistribution of the motion vectors in a model image corresponding to thephotographed scene.

Moreover, the model image is a best shot image of mainly various movingbodies for which movement can be expected. The frequency distribution ofthe motion vectors in these best shot images is the model frequencydistribution of the motion vectors in the model images. A plurality ofmodel frequency distributions of the motion vectors in the model imagesare prepared to correspond to photographic scenes. However, the modelfrequency distribution of the motion vectors in the model images do notcorrespond to all photographic scenes. For usual photography, it isnecessary only to prepare for the photographic scenes which are expectedto be most photographed.

For example, in baseball, the moment when a batter hits, the moment whena player throws the ball or the like. In soccer, the moment when aplayer shoots the ball, the moment of heading, or the like.Alternatively, in a ski jump event, the time while a jumper is airborne,or in speed skating, the time while a skater is traveling in a straightline. Furthermore, in motor sports, the time while an automobile ormotorcycle is traveling a corner.

Further, the model frequency distribution of the motion vectors in amodel image do not all have to be stored in the storage portion 26. Themodel frequency distribution of the motion vectors in a model image canbe taken into the camera 1 as necessary. For example, they can be takeninto the camera 1 by a communication medium such as the internet or awireless LAN, or alternatively via a recording medium such as a memorycard. It is also possible to appropriately set by an operation of theuser which model frequency distributions are taken in.

1.3.4 Specific Example of Method of Judging Similarity

Next, an explanation will be given of the method of judging thesimilarity between a frequency distribution of the motion vectors in animaged image, and the model frequency distribution of the motion vectorsin a model image.

FIG. 13A is a histogram showing a model frequency distribution of themotion vectors in a model image, and FIG. 13B is a histogram showing thefrequency distribution of the motion vectors in an imaged image. FIG.14A is a conceptual drawing showing the case of converting the histogramshown in FIG. 13A into a two dimensional graph. FIG. 14B is a conceptualdrawing showing the case of converting the histogram shown in FIG. 135into a two dimensional graph.

The body side microcomputer 21 segments into a plurality of blocks thedeveloped region of the two dimensional graph. The body sidemicrocomputer 21, as shown in FIG. 14A, segments into a plurality ofblocks K1 to K16 the developed region for the histogram showing themodel frequency distribution of the motion vectors in the model image.In the histogram showing the model frequency distribution of the motionvectors in the model image, peak frequencies are manifested at thepositions of blocks K8, K10 and K16.

The body side microcomputer 21, as shown in FIG. 14B, segments intoblocks M1 to M16 the developed region of the histogram showing thefrequency distribution of the motion vectors in the imaged image. In thehistogram showing the frequency distribution of the imaged image, peakfrequencies are manifested at the positions of blocks M8, M10 and M16.The body side microcomputer 21 judges the similarity for the peakfrequencies of the three peaks included in the respective frequencydistributions. Further, in FIG. 14A and FIG. 14B, the size and positionon the coordinate axis of each block is the same. Accordingly, blocksK8, K10 and K16 respectively correspond to blocks M8, M10 and M16.Further, the numerical values assigned to each block number show thefrequency amount in the peak frequency of each block.

The body side microcomputer 21 calculates the absolute value of thefrequency difference for each corresponding block. The frequency amountof each peak frequency is shown in FIG. 13. For example, the frequencyamount of the peak frequency present at the block K16 in FIG. 13A is“8”. On the other hand, the frequency amount of the peak frequencypresent at the block M16 (corresponding to block K16) in FIG. 13B is“15”. In this case, the frequency difference is “7”. Further, in thecase that there are no respective peak frequencies at the correspondingblock, the frequency difference becomes the frequency amount of the peakpresent at the other block, as is. For example, if there is no peakfrequency present at block M16 of FIG. 13B, the frequency differencebetween block K16 and M16 becomes “8”.

The body side microcomputer 21 calculates the frequency differences foreach block manifesting a peak frequency. Then, it seeks the sum total Σof the absolute values of the frequency differences. The sum total Σ issought according to absolute value of (Kn−Mn). “Kn” is the total of thefrequency amounts of the peak frequencies present at the blocks K8, K10,and K16. “Mn” is the total of the frequency amounts of the peakfrequencies present at the blocks M8, M10 and M16. In the example ofFIG. 13, the sum total Σ is absolute value of (18−23)=5. This sum totalΣ is an indicator when judging the similarity.

When comparing the histograms of FIG. 13A and FIG. 13B FIG. 13A and FIG.13B, if the frequency amounts of the peak frequencies are the same, thefrequency difference is zero. On the other hand, in the case that thefrequency amounts of the peak frequencies differ, a positive or negativevalue is computed for the frequency difference. The more the frequencyamounts of the peak frequencies differ, the greater the frequencydifference. Accordingly, the lower the sum total Σ, the higher thesimilarity, and the higher the sum total Σ, the lower the similarity.

The body side microcomputer 21 judges the similarity by comparing thesum total Σ and a standard value set beforehand. The standard values areset stepwise. As an example, if sum total Σ<20, the evaluation is“high”; if 20≦sum total Σ≦50, the evaluation is “medium”; and if 50< sumtotal Σ, the evaluation is “low”. The standard values may also be morefinely set. Further, they may also be changed automatically or by a usersetting.

In the judgment of similarity described above, in the case that aplurality of images have a high evaluation, a further process to selectone image among them may be added. Below, explanations are given for twospecific examples.

1.3.5 Specific Example of Image Selection (1)

FIG. 15A, FIG. 15B and FIG. 15C are explanatory drawings showingexamples of images which have been highly evaluated in an evaluationusing a frequency distribution of a model image. Each of the imagesshown in FIG. 15A, FIG. 15B and FIG. 15C is an automobile 104 which isthe main subject in the picture 101, recorded with the same size andfrom the same photographic angle. In the evaluation using the frequencydistribution of a model image, the three images shown in FIG. 15A, FIG.15B and FIG. 15C all had a high evaluation. In this case, the body sidemicrocomputer 21 sets regions having a predetermined size in thevicinity of the center of the picture 101. In FIG. 15, an example isshown where a region 103 having a size of about 30% of the picture 101is set in the vicinity of the center of the picture 101.

Next, the body side microcomputer 21 calculates the frequencydistribution of the motion vectors in the image within the region 103based on the frequency distribution of the motion vectors in the imagedimage (image of the picture 101). Further, the body side microcomputer21 calculates the model frequency distribution of the motion vectors inthe model image within the region 103 based on the model frequencydistribution of the motion vectors in the model image stored beforehand.

Then, the body side microcomputer 21 compares the frequency distributionof the motion vectors in the image in the region 103 with the modelfrequency distribution of the motion vectors in the model image in theregion 103, and evaluates the image based on their similarity. Namely,the body side microcomputer 21 gives a high evaluation to an imagehaving a frequency distribution with a high similarity to the modelfrequency distribution of the motion vectors in the model image, andadds a high number of points. In the example shown in FIG. 15, thefrequency distribution of the motion vectors in the image of 152 is thefrequency distribution with a high similarity to the model frequencydistribution of the motion vectors in the model image in the region 103.Because of this, the body side microcomputer 21 gives a high evaluationto the image of FIG. 15B, and adds a high number of points.

Further, the body side microcomputer 21 may also use a model frequencydistribution of the motion vectors in a model image stored beforehand inthe storage portion 26 in the above evaluation. This is a modelfrequency distribution wherein a model image assumed to be a preferableimage is set, within the region 103 of the vicinity of the center of thepicture 101.

According to the present embodiment, it is possible to highly evaluatean image where the main subject is present in the center portion of thepicture even when the size of the main subject within the picture andthe angle of photography are the same.

1.3.6 Specific Example of Image Selection (2)

FIG. 16A and FIG. 16B are explanatory drawings showing examples ofimages which have been highly evaluated in an evaluation using afrequency distribution of a model image. In each of the images shown inFIG. 16A and FIG. 16B, the skater who is the main subject is recorded atthe same position and at about the same size. In FIG. 16A and FIG. 16B,the left side drawing shows the imaged image, and the right side drawingshows the positions of the main blocks and sub-blocks within thesegmented picture. In the left and right drawings, the picture 101 issegmented with the same proportions.

The body side microcomputer 21, for each image, divides the picture 101into a plurality of blocks. Then, for each image, it specifies theblocks (below referred to as main blocks) which are in the vicinity ofthe center of the picture 101, and further which are not background.Next, the body side microcomputer 21 specifies blocks (below referred toas sub-blocks) which resemble the main blocks in color, and further havecolor continuity.

In FIG. 16, block A is specified as a main block. Block A in the presentexample is constituted of 3 blocks. In FIG. 16A, A1 is specified as asub-block. In FIG. 16B, A1 to A5 are specified as sub-blocks.

Next, the body side microcomputer 21 calculates the frequencydistribution of the motion vectors in the image of the sub-blocks foreach image, based on the frequency distribution of the motion vectors inthe imaged image (the image of the picture 101). In the image of FIG.16A, the frequency distribution of the motion vectors in the image ofsub-block A1 is calculated. In the image of FIG. 163, the frequencydistribution of the motion vectors in the image of sub-blocks A1 to A5is calculated.

The body side microcomputer 21 reads out from the storage portion 26 themodel frequency distribution of the motion vectors in a model image inthe sub-blocks. The model frequency distribution of the motion vectorsin a model image in the sub-blocks is a model frequency distribution ofthe motion vectors in a model image of a sub-block (below referred to assub-block model image) with a color resembling the main block, andfurther, having continuity of color, in a model image which is assumedto be a preferable image.

Then, the body side microcomputer 21 compares the frequency distributionof the motion vectors in each sub-block of the image of FIG. 16A andFIG. 16B with the model frequency distribution of the motion vectors inthe sub-block model image, and evaluates the image based on theirsimilarity. In this evaluation, an image having a frequency distributionwith a high similarity to the model frequency distribution of the motionvectors in the sub-block model image becomes an image with a highevaluation.

In sports photographs, it is assumed that a person who is the mainsubject is positioned in the vicinity of the center of the image, and itcan be considered that the main block corresponds to the torso of aperson, and the sub-blocks correspond to the limbs of the person. Thus,in sports photography, if it is taken that an instant where the limbs ofa person make a large movement presents a good scene, then the greaterthe number of sub-blocks which are continuous with the main block, andthe more the sub-blocks extend towards the periphery of the picture, thebetter the scene. In the present embodiment, the model frequencydistribution of the motion vectors in the sub-block model image are setbased on the number and position of sub-blocks in an image capturedduring an instant when the limbs of a person made a large movement asstated above.

Accordingly comparing the frequency distribution of the motion vectorsin the sub-blocks, and the model frequency distribution of the motionvectors in the sub-block model image of the two images in FIGS. 16A and16B, the frequency distribution of the motion vectors in the image ofFIG. 16B where the limbs of the skater who is the main subject make alarge extension, is a frequency distribution with a high similarity tothe model frequency distribution of the motion vectors in the sub-blockmodel image. Because of this, the body side microcomputer 21 evaluatesthe image of FIG. 16B more highly than the image of FIG. 16A, and adds alarge number of points.

According to the present embodiment, even when the size and photographicangle in the picture of the main subject are the same, it is possible tomore highly evaluate an image where the movement of the main subject islarge. Namely, the larger the movement of the main subject, the greaterthe manifestation of characteristic changes in the frequencydistribution of the motion vectors in the sub-blocks. Because of this,by carrying out an evaluation using the model frequency distribution ofthe motion vectors in the sub-block model image, it is possible tohighly evaluate an image having greater movement of the main subjecteven for images where the main subject is recorded at the same positionand at about the same size.

Further, the body side microcomputer 21 may also calculate the modelfrequency distribution of the motion vectors in the sub-block modelimage based on the model frequency distribution of the motion vectors ina model image stored beforehand. Further, the number of blocks whichbecome main blocks is not limited to three. The number of blocks whichbecome main blocks may be 1, or may be 4 or more. The number of theblocks which become main blocks will differ depending on the number ofsegments of the picture 101 and the size of the main subject.

1.3.7 Evaluation Using Histogram of Model Image (2)

FIG. 17A is a conceptual drawing showing the frequency distribution ofthe motion vectors, and its dispersion, in an imaged image. FIG. 17B isa conceptual drawing showing a model frequency distribution, and itsdispersion, in a model image. In the evaluation method of the presentembodiment, the evaluation is carried out based on the similarity of thedispersion of the frequency distribution of the motion vectors in theimaged image and the dispersion of the frequency distribution in a modelimage.

First, the body side microcomputer 21 computes a frequency distributionof the motion vectors in the imaged image, and generates a histogramshowing the frequency distribution of the motion vectors. Here, ahistogram showing the frequency distribution of the motion vectors suchas that shown in FIG. 17A is generated. In FIG. 17A, five peakfrequencies showing the characteristics of the image (circles drawn withsolid lines) are manifested.

Next, the body side microcomputer 21 calculates a peak frequency (belowreferred to as the average peak frequency) which is the average of thefive peak frequencies included in the histogram. In FIG. 17A, theaverage peak frequency is shown with the circle drawn with a dottedline, assigned the reference letter A.

Next, the body side microcomputer 21 computes the difference betweeneach of the five peak frequencies, and the average peak frequency A.This difference is referred to as the distance from the average peakfrequency A to each peak frequency. Further, the body side microcomputer21 totals the differences between the five peak frequencies and theaverage peak frequency. This total value is the dispersion of thefrequency distribution of the motion vectors in the imaged image. Thehigher this dispersion, the more a plurality of subjects are moving indifferent directions with different speeds.

Next, the body side microcomputer 21 reads out the dispersion of themodel frequency distribution of the motion vectors in the model imagefrom the storage portion 26. This is the dispersion in a model imagewhich is assumed to be a preferable image. Also for the model image, thedispersion is sought by calculating the difference between the peakfrequencies and the average peak frequencies.

Then, the body side microcomputer 21 compares the dispersion of thefrequency distribution of the motion vectors in the imaged image and thedispersion of the frequency distribution of the motion vectors of themodel image, and judges their similarity. The body side microcomputer 21gives a high evaluation to an image having a dispersion with a highsimilarity to the dispersion of the model frequency distribution of themotion vectors in the model image, and adds a high number of points. Thebody side microcomputer 21, for example, judges a high degree ofsimilarity if the dispersion of the imaged image is 80 percent or higherwhen the dispersion of the model image is set to 100 percent.

According to the present embodiment, even if the number of peakfrequencies are the same, it is possible to give a higher evaluation toan image wherein a plurality of subjects are moving in severaldirections at several speeds.

Further, the body side microcomputer 21 may calculate the dispersion inthe model image based on the model frequency distribution of the motionvectors in the model image stored beforehand.

Furthermore, the body side microcomputer 21 may give a higher evaluationto an image when the dispersion of the frequency distribution of themotion vectors of the imaged image is equal to or greater than athreshold value. In this case, it is not necessary to judge thesimilarity of the dispersion with the model image. Because of this, thespeed of processing by the body side microcomputer 21 can be increased,and the processing load can be reduced.

Further, the body side microcomputer 21 may extract only peakfrequencies having a frequency amount equal to or greater than athreshold frequency when calculating the average peak frequency A fromthe peak frequencies of the imaged image. In this case, the body sidemicrocomputer 21 computes the average peak frequency A from the peakfrequencies having a frequency amount equal to or greater than thethreshold, and then calculates the dispersion.

1.3.8 Evaluation by Change Amount in Frequency Amount of Peak Frequency

FIG. 18A and FIG. 18B are histograms showing the frequency distributionof the motion vectors in images imaged continuously by the camera 1. Thecamera 1 imaged the images in the order of FIG. 18A and FIG. 18B. In theevaluation method of the present embodiment, the frequency distributionsof the motion vectors in the imaged images are computed, and evaluationof the images is carried out based on the change amount in the frequencyamounts of the peak frequencies included in the frequency distributions.

First, the body side microcomputer 21 computes the frequencydistributions of the motion vectors in the images imaged continuously bythe camera 1 and generates histograms showing the frequencydistributions of the motion vectors. Here, it generates histogramsshowing the frequency distributions of the motion vectors such as thoseshown in FIG. 18A and FIG. 18B. In the histograms shown in FIG. 18A andFIG. 18B, the five peaks A to E showing the characteristics of therespective images are manifested. The body side microcomputer 21extracts the frequency distribution of the motion vectors formed by thepeaks having a frequency amount equal to or greater than a thresholdfrequency from the histograms shown in FIG. 18A and FIG. 18B.

In FIG. 18A, among the five peak frequencies A to E, the peaks A, C andD have a frequency equal to or greater than the threshold frequency th.Further, the peaks B and E have frequencies which do not meet thethreshold frequency th. The body side microcomputer 21 extracts thefrequency distribution of the motion vectors formed by the peaks A, Cand D having frequency amounts equal to or greater than the thresholdfrequency. Next, the body side microcomputer 21 observes the changes, inchronological sequence, in the frequency amounts of the peak frequenciesA, C, and D. Then, the body side microcomputer 21, based on the changeamount in the peak frequencies A, C and D, and an evaluation standardvalue set beforehand, carries out the evaluations of the images. Theevaluation standard value is a value for judging the change amounts, inchronological sequence, in the frequency amounts in the peak frequencieshaving frequencies equal to or greater than the threshold frequency th.

The frequency amounts of the peak frequencies A, C, and D shown in FIG.18A change by increasing as shown in FIG. 18B. The body sidemicrocomputer 21 calculates the average value of the change amount inthe frequency amounts (below referred to as average change amount Δave)for the three peak frequencies A, C, and D. Next, the body sidemicrocomputer 21 compares the calculated average change amount Δave andan evaluation standard value Δth. Then, if the average change amountΔave exceeds the evaluation standard value Δth, the evaluation of thatimage is made high, and a large number of points is added.

Further, the evaluation of an image may be made high in the case thatthe change amount of the frequency amount in at least one peak frequencyamong the three peak frequencies A, C, and D exceeds an evaluationstandard value Δth. Furthermore, the evaluation of an image may be madehigh in the case that the total value of the change amount of increasefor the three peaks A, C and D, is calculated, and this total valueexceeds an evaluation standard value (evaluation standard value forcomparison with the total value).

According to the present embodiment, it is possible to more highlyevaluate an image where, for example even if the subject is moving inthe same way, the size of the subject suddenly increases (an image wherethe area occupied by the subject in the picture is large, underconditions of photographing a subject which is approaching at highspeed). Further, according to this, it is possible to more highlyevaluate an image where for example the moving subjects suddenlyincrease (an image wherein the overall surface area in a picture ofsubjects moving in the same way in an image increases by an increase inthe number of subjects moving in the same way).

1.4 Learning Function of Evaluation Standard

1.4.1 Updating Frequency Distribution of Model Image

In the embodiment of 1.3.3, an explanation was given for the evaluationof an image using a model frequency distribution of the motion vectorsset beforehand as an evaluation standard of a preferable image (modelimage). This model frequency distribution of the motion vectors of themodel image may be updated based on an image selected by the user.

When a user carries out an operation of image playback via an operatingportion, not illustrated, the body side microcomputer 21 displays on thedisplay panel 28 a plurality of images stored in the buffer 25. Theseimages have been assigned an evaluation result. The body sidemicrocomputer 21 displays in order on the display panel 28 a pluralityof images stored in the buffer 25 in response to an operation of theoperating portion, not illustrated, by the user. The user carries out anoperation of selection via the operating portion, not illustrated, whena desired image is displayed on the display panel 28, and this imagebecomes the image selected by the user. The body side microcomputer 21leaves the image selected by the user and its evaluation results in thebuffer 25. Further, the body side microcomputer 21 if necessary deletesfrom the buffer 25 the non-selected images and their evaluation results.Below, an explanation is given of a learning function for updating themodel frequency distribution of the motion vectors of the model imagebased on the image selected by the user.

FIG. 19A is a histogram showing the model frequency distribution of themotion vectors in the model image. FIG. 19B is a histogram showing thefrequency distribution of the motion vectors in the image selected bythe user. FIG. 19C is a histogram showing the model frequencydistribution of the motion vectors in the updated model image. Thereference numbers “K10”, “M8” and the like shown in each drawing showthe block numbers of the developed regions shown in FIG. 14. Further,the numbers assigned to the block numbers show the frequency amounts ofthe peak frequencies of the blocks.

The body side microcomputer 21 updates the model frequency distributionof the motion vectors in the model image based on the trend of thefrequency distribution of the motion vectors in the image selected bythe user. Whether the trend of the frequency distribution of the motionvectors in the image selected by the user is reflected in the modelfrequency distribution of the motion vectors in the model image can beappropriately set. The body side microcomputer 21 carries out acomputation for each peak frequency as an updated frequencyamount=k·A+(1−k)·B. Here, k is a coefficient. A is the frequency amountof the peak frequency in the image selected by the user. B is thefrequency amount of the peak frequency in the model image.

As one example, k is set to 0.3. The frequency amount in the peakfrequency of block No. K8 shown in the histogram of FIG. 19A becomes5×0.7=3.5. The frequency amount of the peak frequency of block No. M8shown in the histogram of FIG. 19B becomes 3×0.3=0.9. Accordingly, asshown by the histogram of FIG. 19C, the updated frequency amount of thepeak frequency of block No. K8 becomes 3.5+0.9=4.4. When a computationis carried out in the same way for block No. K16, as shown by thehistogram in FIG. 19C, the updated frequency amount of the peakfrequency becomes 10.1. For block No. K10, the frequency amount remainsthe same as before the update.

The body side microcomputer 21 updates the model frequency distributionof the motion vectors of the model image based on the image selected bythe user. The body side microcomputer 21 stores the updated modelfrequency distribution of the motion vectors of the model image in thestorage portion 26. At this time, the model frequency distribution ofthe motion vectors of the model image before the update may be retained,or it may be written over (deleted).

In the present embodiment, the model frequency distribution of themotion vectors of the model image is updated based on the image selectedby the user. According to the present embodiment, it is possible tohighly evaluate an image which suits the user's tastes, in an evaluationof an image using a model frequency distribution of the motion vectorsof a model image.

Further, the coefficient k can be appropriately set. In the case thatthe coefficient k is smaller, the characteristics of the model frequencydistribution of the motion vectors of the model image can be moregreatly reflected in the updated frequency amounts. In the case that thecoefficient k is larger, the characteristics of the frequencydistribution of the motion vectors of the image selected by the user canbe more greatly reflected in the updated frequency amounts. In the casethat k is 0.5, the characteristics of each frequency distribution can bereflected as an average in the updated frequency amounts.

In the case that the user selects a plurality of images, the body sidemicrocomputer 21 adds all of the frequency amounts of each peak of thefrequency distribution of the motion vectors in each image selected bythe user and calculates their average value (below referred to as theaverage frequency amount). Then, the body side microcomputer 21calculates the updated frequency amounts based on this average frequencyamount.

Further, in the case that the user selects a plurality of images, themodel frequency distribution of the motion vectors of the model imagemay be updated for each image. For example, it is taken that an image ofa skier jumping in midair (airborne) is stored in the storage portion 26as the model frequency distribution of the motion vectors of the modelimage. The user selects from a plurality of images three images of“instant of jumping”, “airborne”, and “just before landing”. In thiscase, the body side microcomputer 21 will update the model frequencydistribution of the motion vectors in the model image (airborne) basedon the frequency distribution of the motion vectors in the each of thethree images. In this way, it is possible to generate three modelfrequency distributions of the motion vectors of the model images,updated based on the frequency distributions of the motion vectors ofeach of the three images.

The model frequency distributions of the motion vectors in these threemodel images are stored in the storage portion 26 along with the modelfrequency distribution of the motion vectors of the model image(airborne) which becomes the basic model image. In this way, the modelfrequency distribution of the motion vectors in the model image(airborne), which becomes the base image, is updated, and the usercustomizations 1 to 3 are generated.

1.4.2 Updating of the Dispersion

In the embodiment of 1.3.7, an explanation was given for evaluating animage based on the dispersion in a model image. The dispersion of thefrequency distribution in the model image may be updated based on animage selected by the user.

FIG. 20A is a conceptual drawing of a histogram showing the modelfrequency distribution of the motion vectors in a model image. FIG. 20Bis a conceptual drawing of a histogram showing the frequencydistribution of the motion vectors in an image selected by the user.FIG. 20C is a conceptual drawing of a histogram showing the modelfrequency distribution of the motion vectors in a model image where thedispersion has been updated. Each of the drawings of FIG. 20schematically shows the dispersion of the peak frequencies included ineach histogram by an outline of the distribution in order to facilitatethe explanation.

The body side microcomputer 21 updates the dispersion of the modelfrequency distribution of the motion vectors set beforehand as theevaluation standard of a preferable image (model image), based on thedispersion of the frequency distribution of the motion vectors in animage selected by the user. How the dispersion of the frequencydistribution of the motion vectors in the image selected by the user isreflected in the dispersion of the model frequency distribution of themotion vectors in the model image can be appropriately set. For example,a relational expression comprising the respective dispersions andcoefficients may be used, in the same way as in the embodiment of 1.4.1.

In the present embodiment, an explanation will be given for an examplewhere a dispersion which is intermediate between the above twodispersions is calculated. Namely, the body side microcomputer 21calculates a dispersion which is intermediate between the dispersion ofthe model image shown in FIG. 20A and the dispersion of the selectedimage shown in FIG. 20B. In this way, the body side microcomputer 21obtains a dispersion of a model image with an updated dispersion such asthat shown in FIG. 20C. The body side microcomputer 21 stores the modelfrequency distribution of the motion vectors of the image where thedispersion has been updated, in the storage portion 26. At this time,the model frequency distribution of the motion vectors of the modelimage before the update may be retained, or may be overwritten(deleted).

In the present embodiment, the dispersion of the frequency distributionin the model image can be updated based on an image selected by theuser. According to the present embodiment, it is possible to increasethe evaluation of an image which meets the user's preferences in theevaluation of an image using the dispersion of the model frequencydistribution of the motion vectors of a model image.

1.4.3 Updating the Frequency Amounts in the Model Image

In the embodiment of 1.3.7, the frequency amount of the peak frequencyincluded in the frequency distribution of the motion vectors in themodel image may be updated based on an image selected by the user.

FIG. 21A is a conceptual drawing of a histogram showing a modelfrequency distribution of the motion vectors in a model image. FIG. 21Bis a conceptual drawing of a histogram showing the frequencydistribution of the motion vectors in an image selected by the user.FIG. 21C is a conceptual drawing of a histogram showing a modelfrequency distribution of the motion vectors in a model image withupdated peak frequencies. Each drawing of FIG. 21 conceptually shows thepeak frequencies which are distributed most to the outside of therespective frequency distributions.

The body side microcomputer 21 updates the frequency amounts of the peakfrequencies included in the model frequency distribution used whendefining the model frequency distribution of the motion vectors in themodel image, based on the frequency amounts of the peak frequenciesincluded in the frequency distribution of the motion vectors in theimage selected by the user.

In the camera 1 a histogram showing the model frequency distribution ofthe motion vectors in a the model image, such as that shown in FIG.21(a), is stored in the storage portion 26. The body side microcomputer21 extracts the peak frequencies having a frequency amount equal to orgreater than a threshold value based on the frequency distribution ofthe motion vectors in an image selected by the user. By means of this,the body side microcomputer 21 generates a frequency distribution of themotion vectors formed of the peak frequencies having frequency amountsequal to or greater than the threshold frequency, as shown in FIG. 21B.Next, the body side microcomputer 21 extracts the peak frequency havingthe smallest frequency amount from among the frequency distributionshown in FIG. 21B. In FIG. 21B, the peak frequency having the smallestfrequency amount is the frequency amount Py of the peak frequency A.

The body side microcomputer 21 updates the frequency amounts of all thepeak frequencies included in the model frequency distribution of themotion vectors in the model image based on the frequency amount Py ofthe peak frequency A included in the frequency distribution of themotion vectors of the image selected by the user, shown in FIG. 21B.Further, how the frequency amount Py of the peak frequency A should bereflected in the update of the frequency amount of the peak frequenciesin the model image can be appropriately set. For example, the equation:updated frequency amount=k·A+(1−k)·B may be used, in the same way as inthe embodiment of 1.4.1.

The body side microcomputer 21, as shown in FIG. 21C, generates a modelfrequency distribution of the motion vectors in the model image withupdated frequency amounts of all the peak frequencies. The body sidemicrocomputer 21 stores the model frequency distribution of the motionvectors of the updated model image in the storage portion 26. At thistime, the model frequency distribution of the motion vectors of themodel image before the update may be retained, or may be overwritten(deleted).

In the present embodiment, the frequency amounts of the peak frequenciesincluded in the model frequency distribution of the motion vectors ofthe model image are updated based on the frequency amounts of the peakfrequencies included in the frequency distribution of the motion vectorsin an image selected by the user. According to the present embodiment,it is possible to increase the evaluation of an image which meets theuser's preferences in the evaluation of an image using the modelfrequency distribution of the motion vectors of a model image.

1.4.4 Updating the Threshold Frequency

In the embodiment of 1.3.8, the threshold frequency may be updated basedon the frequency amounts of the peak frequencies included in thefrequency distribution of the motion vectors of an image selected by theuser.

For example, in the peak frequencies included in the frequencydistribution of the motion vectors of an image selected by the user, thesmallest frequency amount is set to h1. With respect to this, thethreshold frequency th which was set beforehand is taken to be afrequency amount such that th>h1. In this case, the body sidemicrocomputer 21 calculates the new threshold frequency th′ asth′=th+h1/2. By means of this, the threshold frequency th′ becomes afrequency amount between th and h1. The body side microcomputer 21stores the new threshold frequency th′ in the storage portion 26 as theupdated threshold frequency th.

In FIG. 18A, the frequency amount h2 of the peak frequency E is lessthan the threshold frequency th before the update, and further, is takento be close to th. By reducing the threshold frequency th by the update,the frequency amount of the peak frequency E becomes higher than theupdated threshold frequency th. Because of this, the peak frequency E isextracted along with the peak frequencies A, C, and D.

Further, the threshold frequency th set beforehand may be updated basedon the largest frequency amount in the peak frequencies included in thefrequency distribution of the motion vectors of the image selected bythe user.

According to the present embodiment, it is possible to carry out anextraction to reflect the characteristics of an image preferred by theuser when extracting the peak frequencies included in the frequencydistribution of the motion vectors of the image selected by the user.

Further, in the embodiment according to 1.3.8, the evaluation standardvalue 0th may be updated based on the change amount of the frequencyamount of the peak frequencies included in the frequency distribution ofthe motion vectors of the image selected by the user. For example, inthe average change Δave of the frequency amount of the peak frequencyincluded in the frequency distribution of the motion vectors of theimage selected by the user, the new evaluation standard value Δth′ iscalculated from Δth′=Δave+Δth/2. By means of this, the evaluationstandard value Δth′ becomes a value intermediate between Δave and Δth.The body side microcomputer 21 stores the new evaluation standard valueΔth′ as the updated evaluation standard value Δth in the storage portion26.

When carrying out the updating of the evaluation standard value Δth asdescribed above, the evaluation of the image is carried out as follows.Namely, in the case that the average change Δave of the frequencyamounts in the image selected by the user is less than the evaluationstandard value Δth before the update, the updated evaluation standardvalue is lowered. In this case, even for an image where the movement ofthe subject is not sudden, this image is highly evaluated. Further, inthe case that the average change Lave of the frequency amounts in theimage selected by the user is greater than the evaluation standard Δthbefore the update, the evaluation standard value Δth before the updateis raised. In this case, images with more sudden movement of the subjectwill be more highly evaluated.

According to the present embodiment, the evaluation standard value Δthused when judging the change amount in chronological sequence of afrequency amount is updated based on the average change Δave of thefrequency amounts of the image selected by the user, and therefore, isit possible to carry out the evaluation to reflect the characteristicsof an image preferable to the user.

1.5 Concerning the Learning of Evaluation Methods

1.5.1 Automatic Learning of Evaluation Method

In the present embodiment, an explanation is given for the case ofchanging the weighted evaluation method based on the continuity of theevaluation results.

The body side microcomputer 21 of the present embodiment carries out anevaluation with respect to one image, using evaluation methods A, B, andC which differ from each other. Namely, the body side microcomputer 21evaluates one image by the evaluation methods A, B, and C, and assignsto the image evaluation results for each evaluation method as additionalinformation. The body side microcomputer 21 stores the image with theassigned evaluation results in the buffer 25.

The body side microcomputer 21, for example, executes the reevaluationby the evaluation methods A, B, and C when storing to the memory card 20the image stored in the buffer 25. FIG. 22 is a conceptual drawingshowing the imaged image and its evaluation results. In the initialsettings, the weightings of the evaluation methods are all set to “1”.

As shown in FIG. 22, all of the three continuous images 2, 3 and 4, haveevaluation results according to the method A which are higher than theevaluation results according to the other methods B and C. In this case,the body side microcomputer 21 increases the weighting of the evaluationmethod A to “1.5”. In the present example, the value of the weighting isincreased by 50%. The weightings of the other evaluation methods B and Cremain at “1”. In this way, in the evaluations of the images after 4,the evaluation results according to the evaluation method A are morehighly reflected in the evaluation than the evaluation results accordingto the other methods B and C.

Further, in the evaluation of the images after 4, if a case arises wherethe evaluation results according to a same evaluation method, for threecontinuous images, is higher than the evaluation results according tothe other evaluation methods, the body side microcomputer 21 increasesthe weighting of this evaluation method. Then, the evaluation of thelater images is carried out. Further, in the case that an evaluationresult according to the same evaluation method in the three (orplurality of) continuous images first indicated are higher than theevaluation results according to the other evaluation methods, theweighting of the evaluation method may be changed, without subsequentlychanging the weighting of the evaluation methods.

In the present embodiment, in the case that there are three continuousimages having a higher evaluation result which is higher according to asame evaluation method than the evaluation results according to theother methods (below referred to as the specified images), the weightingof this evaluation method is increased. However, the specified imagesare not limited to three continuous images, and can be arbitrarily set.

According to the present embodiment, evaluations according to apreferable evaluation method which assigns high evaluations to aplurality of continuous images can be reflected in the evaluations ofsubsequent images.

1.5.2 Automatic Learning of Evaluation Method (2)

In the present embodiment, an explanation is given for the case ofchanging the weighting of an evaluation method based on the proportionof high evaluation results assigned, when carrying out the evaluation bya plurality of evaluation methods.

The body side microcomputer 21 of the present embodiment carries out anevaluation with respect to one image, using evaluation methods A, B, andC which differ from each other. Namely, the body side microcomputer 21evaluates one image by the evaluation methods A, B, and C, and assignsto the image evaluation results for each evaluation method as additionalinformation. The body side microcomputer 21 stores the image with theassigned evaluation results in the buffer 25.

The body side microcomputer 21, for example, executes the reevaluationby the evaluation methods A, B, and C when storing to the memory card 20the images stored in the buffer 25. FIG. 23 is a conceptual drawingshowing the imaged images and their evaluation results. In the initialsettings, the weightings of the evaluation methods are all set to “1”.

As shown in FIG. 23, in the five images 1 to 5, the evaluation method B,for image 1, image 3 and image 4, assigned a higher evaluation than theother evaluation methods A and C. Moreover, the proportion of images towhich a high evaluation was assigned, namely the number of images towhich a high evaluation was assigned, is higher than for the otherevaluation methods. In this case, the body side microcomputer 21increases the weighting of the evaluation method B to “1.5” In thisexample, the weighting was increased by 50%. The weightings of the otherevaluation methods A and C remain at “1”. By this means, in theevaluation subsequent to image 5, the evaluation results according tothe evaluation method B are reflected so that the evaluation is higherthan for the evaluation results of the other evaluation methods A and C.After this, evaluation for the five images is carried out in the sameway again, and the weighting of the evaluation methods are changed.

In the present embodiment, the judgment of an evaluation method giving ahigh evaluation was carried out for five images, but without beinglimited to this, it can be arbitrarily set. Further, processing may becarried out as in the following. The body side microcomputer 21 readsout from the buffer 25 a number of images corresponding to from acertain percent of the total to a higher percent of the total. The bodyside microcomputer 21 extracts the evaluation method which gives higherevaluations than other evaluation methods from among these images, andfurther has the largest number of images with high evaluations. The bodyside microcomputer 21 changes the weighting of the extracted evaluationmethod.

According to the present embodiment, an evaluation according to apreferable evaluation method which gives a high evaluation to manyimages can be made to reflect in the evaluation of later images.

1.5.3 Learning of Evaluation Method Based on Image Selected by User

In the present embodiment, an explanation is given for the case ofchanging the weighting of an evaluation method based on an imageselected by the user, when carrying out evaluation by a plurality ofevaluation methods.

The body side microcomputer 21 of the present embodiment, in the sameway as in the embodiment of 1.5.1, carries out an evaluation withrespect to one image, using evaluation methods A, B, and C which differfrom each other. Namely, one image is respectively evaluated by threeevaluation methods, and the evaluation results for each evaluationmethod, as well as the total evaluation results, are added as additionalinformation.

The body side microcomputer 21 stores in the buffer 25 the images withthe attached evaluation results. The body side microcomputer 21 displayson the display panel 28 a plurality of images stored in the buffer 25when the user carries out an image playback operation via the operatingportion, not illustrated. These images have evaluation results attachedto them. The body side microcomputer 21 reads out in order the pluralityof images stored in the buffer 25, and displays them on the displaypanel 28 for only a predetermined time (a plurality of images stored inthe buffer 25 may be displayed in order on the display panel 28 inresponse to an operation of the operating portion, not illustrated, bythe user).

When the user carries out an operation of a selection via the operatingportion, not illustrated, when an image is displayed on the displaypanel 28, that image becomes the image selected by the user. The bodyside microcomputer 21 stores the image for which the selectionindication was made by the user (below referred to as the selectedimage) and its evaluation result in the memory card 20. The body sidemicrocomputer 21 increases the weighting of the evaluation method whichgives a high evaluation to the selected image.

The body side microcomputer 21, after the number of selected images hasreached a predefined number (10 images in the present embodiment),reevaluates the remaining images according to the evaluation methods A,B, and C, with the changed weighting. The body side microcomputer 21selectively displays on the display panel 28 images which have beengiven a high evaluation among the reevaluated images. Namely, after thenumber of images selected by the user has reached 10, the images whichhave been given a low evaluation are not displayed on the display panel28. After this, when an operation of selection by the user for theimages displayed on the display panel 28 is carried out, the body sidemicrocomputer 21 stores the selected images and their evaluation resultsin the memory card 20. Further, the body side microcomputer 21 increasesthe weighting of the of the image evaluation method which gives a highevaluation to the selected images. Then, the body side microcomputer 21repeatedly executes the above processing until there are no imagesstored in the buffer 25.

Next, an explanation is given with reference to the flowchart shown inFIG. 24 of the procedure for the case of changing the weighting of theimage evaluation based on the images selected by the user. Theprocessing of the flowchart shown in FIG. 24 is executed by the bodyside microcomputer 21. In the present embodiment, the weighting of theevaluation method A is W1, the weighting of the evaluation method B isW2, and the weighting of the evaluation method C is W3. In the initialsettings, the weightings of all three of these evaluation methods is“1”. The total evaluation result E for each single image is expressed bythe formula (1) below.E=W1×(evaluation method A)+W2×(evaluation method B)+W3×(evaluationmethod C)  (1)

In Step S201, the body side microcomputer 21 sets to 1 the image No. nread out from the buffer 25. In Step S202, the body side microcomputer21 reads out the image of image No. n from the buffer 25. In Step S203,the body side microcomputer 21 displays on the display panel 28 the readout image. At this time, the body side microcomputer 21 starts clockingby a timer, not illustrated.

In Step S204, the body side microcomputer 21 obtains a user operationvia the operating portion, not illustrated. Here, carrying out aselection indication of an image is taken as the user operation. In StepS205, the body side microcomputer 21 judges whether the clocking by thetimer, not illustrated, has passed a predetermined time. If the judgmentof Step S205 is NO, then the body side microcomputer 21 returns to StepS203. If the judgment of Step S205 is YES, the body side microcomputer21 proceeds to Step S206.

In Step S206, the body side microcomputer 21 judges whether there is aselection indication of an image by the user. If the judgment of StepS206 is NO, the body side microcomputer 21 proceeds to Step S207. InStep S207, the body side microcomputer 21 sets the image number n asn+1, and returns to Step S202. If the judgment of Step S206 is YES, thebody side microcomputer 21 proceeds to Step S208.

In Step S208, the body side microcomputer 21 judges whether there is anevaluation method which gives high evaluations to the selected images.If the judgment in Step S208 is YES, the body side microcomputer 21proceeds to Step S209. In Step S209, the body side microcomputer 21changes the weighting of the evaluation method which gives highevaluations to the selected images. In Step S210, the body sidemicrocomputer 21 reevaluates the remaining images according to Formula(1) with the changed weightings.

In Step S211, the body side microcomputer 21 stores in the memory card20 the selected images and their evaluation results. In Step S212, thebody side microcomputer 21 judges whether there are images remaining inthe buffer 25. If the judgment of Step S212 is YES, the body sidemicrocomputer 21 proceeds to Step S213. In Step S213, the body sidemicrocomputer 21 judges whether the number of selected images is greaterthan 10.

If the judgment in Step S213 is NO, the body side microcomputer 21proceeds to Step S207. Namely, in the case that the number of selectedimages has not reached 10, the body side microcomputer 21 reads out thenext image from the buffer 25. If the judgment in Step S213 is YES, thebody side microcomputer 21 proceeds to Step S214.

In Step S214, the body side microcomputer 21 searches in order theimages stored in the buffer 25 and reads out the images with highevaluations. The body side microcomputer 21 refers to the evaluationresults added to the images, and reads out the images with a totalevaluation result equal to or greater than a predetermined number ofpoints. Following Step S214, the body side microcomputer 21 proceeds toStep S203. By this means, after the number of selected images is 10 ormore, only the images having a high evaluation are displayed on thedisplay panel 28.

If the judgment in Step S212 is NO, the body side microcomputer 21proceeds to Step S215. In Step S215, the body side microcomputer 21 setsthe image number n to zero, and the processing of the present flowchartis concluded.

Further, in the processing according to the above flowchart, regardlessof the number of selected images, all of the images stored in the buffer25 may be read out in order and displayed on the display panel 28.

In the present embodiment, the weighting of the evaluation methods ischanged based on images selected by the user, and the remaining imagesare reevaluated. According to the present embodiment, it is possible togradually display images which agree with the preferences of the user,after a predetermined number of displayed images has been exceeded.Because of this, it is possible to shorten the time for the sorting ofimages by the user when replaying photographed images. Further, insubsequent photography, it is possible to increase the evaluation ofimages which agree with the users preferences.

1.6 Concerning Feedback of the Evaluation Results

The body side microcomputer 21 feeds back to the user evaluation resultsof the photographed images so as to be useful for subsequentphotography. Two embodiments are explained below.

1.6.1 Self-Correction of Photographic Conditions

The present embodiment carries out processing to automatically correctthe photographic conditions during photography, for example whencontinuously photographing a plurality of images while the releasebutton is being held pressed. Because of this, the body sidemicrocomputer 21, as processing of the photographic condition correctionportion, executes processing which corrects the conditions when imagingat the imaging sensor 7, based on evaluation items when there areevaluation items which require correction among the evaluation resultsof a plurality of images stored in the buffer 25. Here, the conditionswhen imaging includes various conditions such as the exposure conditionssuch as the aperture value, shutter speed and the like, focusadjustment, and photographic sensitivity, and the like.

For example, it will be taken that the exposure is outside of thecorrect range in the evaluation results for the first 10 images duringcontinuous photography. The body side microcomputer 21, in the case thatthe points for the evaluation item of the exposure are low,appropriately changes the exposure conditions such as the aperture valueand shutter speed or the like, for the 11th and later photographs, tocorrect so that the exposure becomes suitable. In this way, for example,in the case of carrying out continuous photography of 50 images, even ifthe exposure of the first 10 photographs is not suitable, it is possibleto obtain suitably exposed images for the later 40 photographs. Such acorrection may also be repeatedly executed during continuousphotography, for example every 10 images.

In the present embodiment, an explanation was given for the case ofchanging the exposure conditions, but the focus adjustment, photographicsensitivity, and the like may also be corrected, and a plurality ofconditions may also be corrected at the same time. Further, such acorrection of photographic conditions may be applied to a mode whichcontinuously makes a plurality of photographs with one press of therelease button.

1.6.2 Suggestion of Preferable Settings, Operations and the Like

The present embodiment suggests to the user settings, operations or thelike to photograph the optimal images, based on the evaluation resultsof images. Because of this, the body side microcomputer 21, as aprocessing of a correction information extraction portion, analyzes theevaluation results of images stored in the buffer 25, and executesprocessing to extract correction information to further increase theevaluation results of these images. Next, the body side microcomputer 21executes processing to display on the display panel 28 this correctioninformation. Here, the correction information comprises one or aplurality of items of information, and includes voice and the like, inaddition to message displays of letters and numbers, bar graph displays,graphic displays and the like.

FIG. 25 is a conceptual drawing showing one example of an imageevaluation screen displayed on the display panel 28. On this displaypanel 28, usually the aperture value, shutter speed, photographic modeand the like are displayed. Further, a screen such at that shown in FIG.25 is displayed when the user selects the item of the image evaluationmode in a menu on a setting screen. Below, the display content of eachdisplay area is explained.

The image display area 41 is an area which displays the image which isthe subject of evaluation. The spot frame 41 a of the image display area41 shows the portion assumed to be the main subject. The area displayedby the spot frame 41 a is displayed as an enlarged image in thesub-display area 42. In this way, it is possible for the user to graspwhich portion is assumed to be the main subject.

The histogram display area 43 is a display area of the histogram whichshows the frequency distribution of the color gradation in thephotographed image. At the lower side of this area, the indicator 43 awhich shows the position in the histogram of the portion assumed to bethe main subject is displayed. By this means, it is possible for theuser to confirm the distribution of the color cast, the color bias andthe like of the photographed image.

The assist information display area 44 is an area which, for example inthe case that blurring of an image occurs, displays a message suggestingmaking the shutter speed faster, increasing the photographic sensitivityor setting the blur correction mode or the like. In this way, it ispossible for the user to obtain information concerning the specificoperations and settings for photographing an optimal image. Further, inthe assist information display area 44, a message with specificnumerical values, such as “increase the sensitivity to ISO 800” may bedisplayed.

The evaluation result display area 45 is an area which displays theevaluation results of the image displayed in the image display area 41.As mentioned above, points are added corresponding to respectiveevaluation items for the evaluated image. Here, the display indicatesnot points but, for example, five grades from A to E. By means of such adisplay, it is possible to provide the user with the evaluation resultsof an image in a form which is easy to understand. Further, points ofthe evaluation results, converted to points where 100 is a full score,may also be displayed.

The exposure condition display area 46 is an area which displays anexposure correction value and white balance correction value. In thecase that the evaluation results relating to exposure of a photographedimage are low, for example if there are many regions of overexposure(whiteout), a recommended correction value for exposure correction tothe underexposure side is displayed in the exposure correction area 46a. Further, if there are many regions of underexposure (blackout), arecommended correction value for exposure correction to the overexposureside is displayed in the exposure correction area 46 a. Further, in thecase that the white balance value at a face position is outside of thesuitable range, a recommended correction value for restoring this valueto a suitable range is displayed in the white balance correction area 46b. In this way, the user can know the correction values necessary forthe exposure correction as specific numerical values.

The focus location display area 47 is an area which shows the focusingstate at the position of the main subject. Here, in the evaluation ofthe focus of Step S108 of FIG. 2, in the case that back focus or frontfocus were judged, a suggestion is displayed to fine tune the focusposition of the autofocus to approach the front or rear. In FIG. 25, anexample displaying the adjustment amount and adjustment direction (frontdirection plus, rear direction minus) is shown. By this means, it ispossible for the user to understand the cause of bad focus, and inaddition, can know to what extent it should be adjusted.

In addition to the above embodiment, the edge portions evaluated forfocus and blurring, for example, may be displayed on the image displayarea 41 as a peaking display, so that the user can understand the focusand blurring conditions at one glance. Further, the color coordinates ofthe face portion may be displayed on the sub-display area 42, so thatthe white balance conditions may be visually understood.

Furthermore, in the exposure correction, even if the evaluations of theoverexposure (whiteout) and underexposure (blackout) are suitable, insome cases the exposure of the main subject may be unsuitable. At suchtimes, the assist information display area 44 may carry out a suggestiondisplay to change the settings of the gradation control.

Moreover, in the above explained embodiments, the image evaluationscreen shown in FIG. 25 suggests settings and operations forphotographing optimal images. Because of this, the actual settings andoperations are carried out through a separate setting screen or thelike. For example, the adjustment of the autofocus position is set andregistered by selecting autofocus fine tuning setting screen.

The camera 1 according to embodiment 1 has the effects described below.Further, the camera 1 according to embodiment 1, in addition to theeffects described below, has various effects stated in the embodiments.

(1) The evaluations are made based on the changes in chronologicalsequence in the characteristics in the photographic frame whenpluralities of image frames are imaged. Because of this, compared to amethod which evaluates an image by noting the partial characteristics ofan image of a face or the like, it is possible to comprehensivelyevaluate the entirety of an image. As a result, it is possible to carryout better image evaluation.

(2) Image information stored in the buffer 25 is evaluated ascharacteristic amounts of the motion vectors arising in the frame ofeach block when the photographed image is segmented into a plurality ofblocks; thus the case of photographing moving subjects in particular canbe effectively evaluated.

(3) The image information stored in the buffer 25 may be evaluated as acharacteristic amount of the defocus amount generated between frames ofeach block when segmenting the photographed images into a plurality ofblocks. In this case, an effective evaluation may be carried out for thecase of desiring to photograph an image where focusing in particular isprioritized.

(4) The image information stored in the buffer 25 is evaluated based onthe characteristic amounts of the motion vectors and defocusinformation, and on the position of the main subject. Because of this, amore comprehensive evaluation can be carried out. In this case, a moreeffective evaluation can be carried out in particular for the case ofphotographing people as the main subject.

(5) The body side microcomputer 21 adds the evaluation results of anevaluated image as additional information and stores this in the buffer25. Because of this, it is possible to refer to the evaluation resultsnot only when photographing, but also after photographing.

(6) When selecting at least one image whose evaluation result points areequal to or greater than a predetermined threshold value, from among aplurality of images stored in the buffer 25, this image and itsevaluation results (or these images and their evaluation results) remainin the buffer 25, and when the evaluation results for the other imagesare deleted from the buffer 25, unnecessary image data can be reduced,and thus the storage area of the buffer 25 can be effectively used.

(7) When an evaluation item which requires correction is present amongthe evaluation results in a plurality of images stored in the buffer 25,when this condition is corrected at the time of imaging at the sensor 7based on this evaluation item, it is possible to reduce the imageshaving low evaluations. In particular, it is possible to prevent thephotographing of many images having low evaluations in the case thatpluralities of images are continuously photographed at once.

(8) When analyzing the evaluation results of the images stored in thebuffer 25, extracting the correction information for increasing theevaluation results of these images, and displaying this correctioninformation on the display panel 28, it is possible to make suggestionsto the user for photographing better images. In this way, by not onlyproviding feedback of the evaluation results to the user, but alsosuggesting hints and advice for photographing better images, it ispossible for the user to photograph images with higher evaluations.

Embodiment 2

Next an explanation is given of an example of the application of theimage evaluation apparatus according to the present invention applied toa camera where the lens is built into the camera body, or integratedwith the camera body. Further, in the following explanation, theportions which are the same as those of embodiment 1 are explainedassigning the same reference numbers.

2. Embodiment 2

2.1 Constitution of the Camera

FIG. 26 is a block diagram showing the functional constitution of acamera according to embodiment 2. In the camera 1A shown in FIG. 26, theoperations of each portion relating to the focus adjustment, photometry,and exposure are controlled by a single microcomputer 29. Further, theimaging sensor 7 is provided with functions not only for photometry(exposure), but also for focus detection, and as an electronic shutter.Accordingly, the microcomputer 29 in the present embodiment is providedwith functions as the focus detection portion 22 and the shutter controlportion 23 of FIG. 1.

2.2 Concerning the Imaging Procedure and Operation

Next, an explanation is given referring to the flowchart of FIG. 27 ofthe series of the procedures from the photographing of an image to thestorage, in the camera 1A of the embodiment 2 constituted as describedabove. The processing of the flowchart shown in FIG. 27 is executed bythe microcomputer 29. The processing based on this flowchart is startedwhen the user selects the item of the image evaluation mode from asetting screen, not illustrated, and then half presses the releasebutton, not illustrated. In the following explanation, processing whichis the same as that for embodiment 1 is shown with the step numberscorresponding to those of embodiment 1, and overlapping explanations areomitted.

In Step S301, the microcomputer 29 images (exposes) an image at theimaging sensor 7. Here, the imaged image is output to the imageprocessing portion 24 as an image signal.

The processing of the microcomputer 29 in Step S302 to Step S305 is thesame as the processing of Step S103 to Step S106 in FIG. 2.

In Step S306, the microcomputer 29 carries out the exposure computationsconsidering the luminance of the main subject together with theluminance information calculated in Step S302, using the location of themain subject sought in Step S305. The microcomputer 29 determines theaperture value and shutter speed based on the suitable exposure valuesought by the exposure computation. Then, the microcomputer 29 carriesout the aperture adjustment controlling the aperture control portion 17based on the aperture value. Further, the microcomputer 29 adjusts theexposure amount at the imaging sensor 7 based on the shutter speed.

In Step S307, Step S308, and Step S309, the processing of themicrocomputer 29 is the same as the processing of Step S108, Step S113,and Step S114 of FIG. 2.

In Step S310, the microcomputer 29 judges whether the release button,not illustrated, is fully pressed. If the judgment in Step S310 is NO,the microcomputer 29 proceeds to Step S312. If the judgment in Step S310is YES, the microcomputer 29 proceeds to Step S311. In Step S311, themicrocomputer 29 records in the memory card 20 the image stored in thebuffer 25.

The camera 1A is provided with a photography mode which records anindicated number of images in the interval between the half press of therelease button until the full press of the release button. The presentflowchart shows the processing for the case of selecting thisphotography mode. In this photography mode, even while the releasebutton is half pressed, images are continuously recorded, thus it isnecessary to ensure the storage capacity of the buffer 25. Because ofthis, the microcomputer 29 deletes old images from the buffer 25 in theorder of time elapsed since recording, when a predetermined number ofimages stored in the buffer 25 is exceeded. In this way, a predeterminednumber of images is constantly recorded in the buffer 25. Then, themicrocomputer 29, when the release button 25 is fully pressed, stores inthe buffer 25 a number of images before the full press, and apredetermined number of images after the full press. The microcomputer29, in the case that the full press of the release button is released,stores in the memory card 20 the images stored in the buffer 25.

In Step S312, the microcomputer 29 judges whether the release button isbeing half pressed. If the judgment in Step S312 is NO, the processingof the present flowchart is finished. On the other hand, if the judgmentin Step S312 is YES (here, full pressing is also included), themicrocomputer returns to Step S301.

The camera 1A according to embodiment 2 has the same effects asembodiment 1. Further, it is also possible to similarly apply theevaluation methods and learning functions explained for embodiment 1.However, there will be partial differences in the processing sequenceand content arising from the structural differences with a single lensreflex camera.

Modifications

The present invention is not limited to the embodiments explained above,and may be subjected to various changes and modifications as shownbelow, and these are also within the scope of the present invention.

In embodiment 1, the evaluation of an image is carried out during thehalf press of the release button. In contrast, it is also possible tocarry out only the various computations necessary in the evaluation ofan image during the half press of the release button, and carry out theevaluation of an image stored in the buffer 25 after the full press ofthe release button.

In embodiment 1, a constitution is shown wherein the lens drivingportion 15 is provided inside the lens barrel 3. In contrast, theconstitution may be such that the lens driving portion 15 is provided atthe camera body 2. In this case, the driving power of the lens drivingportion 15 is transmitted to the photographic lens 13 via a coupling,not illustrated. Further, the control of the lens side microcomputer 18may be executed by the body side microcomputer 21.

Furthermore, the evaluation may be increased for an image where theabsolute distance (length) from the peak frequency positioned furthestfrom the origin (the origin point of the histogram distribution fromwhich motion components of the camera such as hand shake and the likehave been deleted from each histogram distribution) and the origin,among the positions in the histogram distribution coordinates of thepeak frequencies in the histogram distribution of the motion vectors, isrelatively large compared to the other images. In this way, is itpossible to increase the evaluation of images where the subject suddenlychanges in the picture (movement, changes in shape, and the like). Tospecifically explain this using a drawing, for example in FIG. 18A thepeak frequency C is the peak frequency most separated from the origin,and therefore, the absolute distance in this image becomes the lengthconnecting the origin and the peak frequency C in a straight line.Further, while not illustrated, in the case that there were anotherimage forming a histogram having a peak frequency at a position afurther distance from the origin than the peak frequency C, this imagewould be evaluated with a relatively higher evaluation.

The image evaluation apparatus according to the present invention mayalso be installed on a personal computer. This personal computer isprovided with, in addition to a monitor apparatus displaying the image,an I/F portion which inputs image files to a memory card via acommunication line.

Further, embodiments 1 and 2, and the modifications and changes, may beused in appropriate combinations thereof, but detailed explanationsthereof are omitted here. Further, the present invention is not limitedby the above embodiments.

What is claimed is:
 1. An image evaluation apparatus comprising: astorage portion which stores a plurality of images when a photographingarea of a camera has continuously imaged in a predetermined timeinterval a plurality of frames of a subject, and an image evaluationportion which evaluates a plurality of the images stored in the storageportion, based on a characteristic amount showing a state in which thesubject is changing in chronological sequence, wherein: the imageevaluation portion, from a plurality of the images stored in the storageportion, calculates a number of peak frequencies which form a frequencydistribution of motion vector arising between frames of each segmentedregion when each image is segmented into a plurality of segmentedregions, and which have a frequency equal to or greater than a thresholdfrequency, and carries out the evaluation based on the number of thepeak frequencies, and the image evaluation portion gives a relativelyhigher evaluation with respect to other images, for an evaluation of animage imaged within a predetermined time from the change of the numberof the peak frequencies.
 2. A non-transitory computer-readable storagemedium that stores a computer-executable program for an image evaluationapparatus which comprises a storage portion and an image evaluationportion, the program comprising instructions for: storing a plurality ofimages when a photographing area of a camera has continuously imaged ina predetermined time interval a plurality of frames of a subject, thestep being performed by the storage portion, evaluating a plurality ofthe images stored in the storage portion, based on a characteristicamount showing a state in which the subject is changing in chronologicalsequence, the step being performed by the image evaluation portion, froma plurality of the images stored in the storage portion, calculating anumber of peak frequencies which form a frequency distribution of motionvector arising between frames of each segmented region when each imageis segmented into a plurality of segmented regions, and which have afrequency equal to or greater than a threshold frequency, and carryingout the evaluation based on the number of the peak frequencies, the stepbeing performed by the image evaluation portion, and giving a relativelyhigher evaluation with respect to other images, for an evaluation of animage imaged within a predetermined time from the change of the numberof the peak frequencies, the step being performed by the imageevaluation portion.